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Papers dealing with vegetation segmentation from RGB images confounded the effect of color representation with a clustering algorithm. In this paper, various color representation of RGB images are compared. On a set of images, vegetation and soil pixels were manually isolated to form two known populations for each image. For each pixel in the populations, Mahalanobis distances were computed and each pixel was assigned to the population (soil or vegetation) corresponding to the smallest Mahalanobis distance. The comparison between color representation methods was based on classification errors. Two image sets were used. One was under controlled flash illumination and the other one was under uncontrolled outdoor lighting. Images acquired under controlled illumination were transformed to simulate illuminants corresponding to other correlated color temperature covering most situations that can be encountered under natural lighting in the field. Under controlled illumination, the choice of the color representation method was not critical. Under uncontrolled outdoor lighting, color representations that remove completely intensity information performed better.