Spatial structure in a color image can be represented using correlation functions defined within and between sensor bands. Using a linear model for surface spectral reflectance with the same number of parameters as the number of classes of photoreceptors, we show that illumination changes correspond to linear transformations of a surface correlation matrix. From this relationship, we derive a distance function for comparing sets of spatial correlation functions that can be used for illumination-invariant recognition. We demonstrate using a large body of experiments that this distance function can be used for accurate texture classification in the presence of large changes in illumination spectral distribution.