The paper reports the development of a classifier that can accurately and reliably discriminate among a large number of different natural surfaces in canonical and natural color images regardless of the viewpoint and illumination conditions. To achieve this objective, a set of general-purpose color and texture features were identified as the input to an ALISA statistical learning engine. These general-purpose color and texture features are those which exhibit the least sensitivity to illumination and viewpoint variation in a broad range of applications. To overcome the Bayesian confusion while a large number of test classes are involved, an ALISA dCRC classification method is developed. The classifier selects the trained class which has a known reclassification distribution histogram of a training image patch that is most closely matched with the unknown classification distribution of the test image patch. Preliminary results using the CUReT color texture dataset with test images not in the training set yields average classification accuracies well above 95% with no significant associated cost in computation time.