Short answer
Basic colour terms are the small set of short, general colour words every speaker of a language knows and uses for anything — in English usually counted as eleven: black, white, red, green, yellow, blue, brown, purple, pink, orange and grey. Languages differ in how many they have and where their boundaries fall, but not at random: fifty years of research, from Berlin and Kay's 1969 study to the World Color Survey of 110 languages, finds strong shared tendencies alongside real variation, and how to explain both is still debated.
Berlin and Kay's 1969 book Basic Color Terms proposed tests for separating basic words from the rest. A basic term is a single word rather than a compound ('green', not 'light green' or 'bluish'); its meaning is not included in another colour word's (crimson is a kind of red, so it is not basic); it is not restricted to a narrow class of things (blond applies to hair and little else); and it is salient — frequent, known to everyone, and readily used. By those tests English has eleven. Every other word in this lexicon — teal, mauve, taupe, burgundy — is secondary: named after a thing, restricted, or a subdivision of a basic term. The distinction matters because secondary words are where speakers disagree most.
Berlin and Kay compared colour words in around twenty languages and argued that basic terms are drawn from a universal set of eleven categories, added in a broadly fixed order: languages with two terms split dark-cool from light-warm; a third term is red; then green and yellow; then blue; then brown; then purple, pink, orange and grey. The claim prompted the World Color Survey, run from Berkeley from the late 1970s: an average of 24 native speakers of each of 110 unwritten languages named every chip in a 330-chip Munsell array and picked the best examples of their terms. Its data were digitised and made public in the early 2000s and underpin most later work.
Critics have questioned Berlin and Kay's sampling and assumptions, and the strict evolutionary order has been loosened considerably. Later work reframed the question. Regier, Kay and Khetarpal (2007) showed that colour categories across languages look like near-optimal divisions of an irregularly shaped perceptual colour space — explaining shared tendencies without requiring fixed universal categories. Gibson and colleagues (2017) found that in every World Color Survey language warm colours are communicated more precisely than cool ones, and linked this to which colours matter for objects. Twomey and colleagues (2024) showed that a language's existing vocabulary constrains which efficient system it moves to next. The field now treats both universal pressure and history as real.
Some languages have twelve basic terms, splitting what English calls blue: Russian goluboy (light blue) and siniy (dark blue) are the standard example. Winawer and colleagues (2007) found Russian speakers were faster to tell two blues apart when the pair crossed that boundary, and that a verbal distraction task removed the advantage. A 2020 re-run of the same tasks by Martinovic, Paramei and MacInnes found the effect's limits. The careful conclusion is that words can speed some judgements at a boundary, not that speakers of different languages see different colours. Individual speakers of one language also differ: in a 2023 study of English- and Somali-speaking pairs, naming varied considerably between people, yet listeners still identified the intended sample better than the naming data alone predicted. All of this is why Colourwise does not treat a colour word in one language as the exact equivalent of one in another.
Run the lexicon's eleven basic regions across a grid of 5,832 sRGB colours and 63% of the grid falls in none of them. That is partly an artefact of drawing regions conservatively around cited anchors — real speakers stretch basic terms to cover almost everything, which the table's last column imitates by giving every colour to its nearest basic region — but it shows where the stretching happens. The secondary words whose regions no basic term claims at all include turquoise, aqua, mint, aquamarine, teal, cyan, khaki, olive, peach, lilac, navy, periwinkle: the blue-green band, the yellow-greens, the pale tints between the basic hues and the darkest reds. Those are the gaps, and they are where the arguments about names on the boundary pages of this section happen.
| Basic term | sRGB grid colours inside | Share of the grid inside | Share if every colour goes to its nearest basic term |
|---|---|---|---|
| Black | 4 | 0.1% | 1.6% |
| White | 3 | 0.1% | 1.3% |
| Red | 271 | 4.6% | 9.0% |
| Green | 790 | 13.5% | 25.6% |
| Yellow | 82 | 1.4% | 4.5% |
| Blue | 452 | 7.8% | 14.8% |
| Brown | 109 | 1.9% | 3.6% |
| Purple | 176 | 3.0% | 14.5% |
| Pink | 72 | 1.2% | 5.1% |
| Orange | 99 | 1.7% | 4.5% |
| Grey | 72 | 1.2% | 15.3% |
| Term | sRGB grid colours inside | Share not claimed by any basic term |
|---|---|---|
| Turquoise | 120 | 100% |
| Aqua | 110 | 100% |
| Mint | 83 | 100% |
| Aquamarine | 73 | 100% |
| Teal | 69 | 100% |
| Cyan | 69 | 100% |
| Khaki | 57 | 100% |
| Olive | 50 | 100% |
| Peach | 44 | 100% |
| Lilac | 42 | 100% |
| Navy | 38 | 100% |
| Periwinkle | 37 | 100% |
Why: A colour word was treated as an exact equivalent of a word in another language whose boundaries differ.
Fix: Translate colour descriptions with a value or a swatch attached, and check boundary cases (blue/green, light/dark blue) with a native speaker.
Why: Words like teal or mauve are not basic, and speakers disagree about them.
Fix: Use basic terms plus a modifier ('dark blue-green') for labels meant for everyone, and reserve secondary names for flavour.
Each statement is labelled by kind — established fact, a standard’s requirement, observed market data, a convention, or Colourwise’s own interpretation or analysis — with the strength of the evidence behind it.
FactStrong evidence
The World Color Survey collected colour-naming and best-example data from an average of 24 native speakers of each of 110 unwritten languages, using a 330-chip Munsell array.
Source: World Color Survey data archives; Color naming reflects optimal partitions of color space (PNAS 104: 1436–1441, 2007)
FactModerate evidence
Berlin and Kay (1969), comparing colour words in about twenty languages, identified eleven basic colour terms in English and proposed that languages acquire basic terms in a broadly fixed sequence.
Caveat: The fixed sequence has since been loosened; later work treats it as a set of tendencies rather than a law.
Source: Color term (Wikipedia); World Color Survey data archives
FactStrong evidence
Colour naming across languages is consistent with near-optimal partitions of an irregularly shaped perceptual colour space, which accounts for universal tendencies and some cross-language variation.
Caveat: A model fit to naming data; it does not by itself explain why a particular language drew its lines where it did.
Source: Color naming reflects optimal partitions of color space (PNAS 104: 1436–1441, 2007); Focal colors across languages are representative members of color categories (PNAS 113: 11178–11183, 2016)
FactStrong evidence
Across the World Color Survey languages, warm colours are communicated more efficiently than cool colours.
Caveat: An information-theoretic measure on naming data; the link to object colours is the authors' proposed explanation.
Source: Color naming across languages reflects color use (PNAS 114: 10785–10790, 2017)
FactModerate evidence
Russian speakers discriminated blues faster across the goluboy/siniy boundary in one study, but a later re-run of the same tasks found clear limits to that effect.
Caveat: The size and robustness of language effects on colour discrimination remain contested; neither study shows that speakers perceive colours differently in general.
Source: Russian blues reveal effects of language on color discrimination (PNAS 104: 7780–7785, 2007); Russian blues reveal the limits of language influencing colour discrimination (Cognition 201: 104281, 2020)
Colourwise analysisLimited evidence
63% of a 15-step sRGB grid falls outside all eleven basic-term regions in the Colourwise lexicon.
Based on: Each grid colour tested against the lexicon's basic-term regions, which are editorial ranges drawn around cited anchors.
Caveat: Reflects how conservatively the regions are drawn, not a finding about English speakers, who extend basic terms far more widely.
Source: CSS Color Module Level 4; Color survey results, and the rgb.txt list of the 954 most common colour names
Reviewed 29 September 2026. Colourwise summarises its sources in its own words and does not reproduce standards text or proprietary colour data. Spotted an error? Tell us.