We represent texture in a color image using spatial correlation functions defined within and between sensor bands. This representation has been shown to be useful for surface recognition, but the structure of spatial correlation functions depends on the spectral properties of the scene illumination. 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. This distance function can be computed efficiently from estimated correlation functions. We demonstrate using a large body of experiments that this distance function can be used for accurate surface recognition in the presence of large changes in illumination spectral distribution.