A bidirectional texture function (BTF) describes image texture as it varies with viewing and illumination direction. Many real world surfaces such as skin, fur, gravel, etc. exhibit fine-scale geometric surface detail. Accordingly, variations in appearance with viewing and illumination direction may be quite complex due to local foreshortening, masking and shadowing. Representations of sur-face texture that support robust recognition must account for these effects. We construct a representation which captures the underlying statistical distribution of features in the image texture as well as the variations in this distribution with viewing and illumination direction. The representation combines clustering to learn characteristic image features and principle components analysis to reduce the space of feature histograms. This representation is based on a core image set as determined by a quantitative evaluation of importance of individual images in the overall representation. The result is a compact representation and a recognition method where a single novel image of unknown viewing and illumination direction can be classified efficiently. The CUReT (Columbia-Utrecht reflectance and texture) database is used as a test set for evaluation of these methods.