A model-based texture recognition system that classifies image textures seen from different distances and under different illumination directions is presented. The system works on the basis of a surface model obtained by means of four-source color photometric stereo (CPS), used to generate 2-D image textures as they would have appeared if imaged under different imaging geometries. The proposed recognition system combines cooccurrence matrices for feature extraction with a nearest neighbor classifier. The use of the cooccurrence matrices instead of filtering methods for feature extraction allows us to utilize only pixels for which valid information has been extracted by CPS. The validity of the method is demonstrated by classifying texture images captured under different imaging geometries than the reference images in the database. Moreover, the process of recognition allows one to guess the approximate direction of the illumination used to capture the test image.