In computer vision, colour naming has been posed as a fuzzy-set problem where each colour category is modeled by a function that assigns a membership value to any given sample. However, the success in the automation of this process relies on having an appropriate psychophysical data set for this purpose. In this article we present a data set obtained from a colour-naming experiment. In this experiment, we used a scoring method to collect a set of judgments adequate for the fuzzy modeling of the colour-naming task. The data set is composed of 387 colour reflectances, their CIELab and Munsell values, and the corresponding judgments provided by the subjects in the experiment. These judgments are the membership values to the 11 basic colour categories proposed by Berlin and Kay (Berlin B, Kay P. Berkeley: University of California; 1969). All these data have been made available online (http://ww.cvc.uab.es/color-naming) and, in this article we provide a wide analysis of them. To prove the suitability of the proposed scoring methodology, we have computed a set of common statistics in colour-naming experiments, such as consensus and consistency, on our data set. The results make it Possible for us to conclude the coherence of our data with previous experiments and, thus, its usefulness for the fuzzy modeling of colour naming. (C) 2005 Wiley Periodicals, Inc.