In this work we present an effective framework for color-texture classification where statistical features are computed from a generalized isotropic co-occurrence matrix extracted from color bands and combined with image entropies. The proposed approach has been effectively tested in RGB, HSL and La*b* color spaces. The tests were conducted in a variety of industrial samples. The obtained results are promising and show the possibility of efficiently classifying complex industrial products based on color and texture features.