Texture as a surface representation is the subject of a wide body of computer vision and computer graphics literature. While texture is always associated with a form of repetition in the image, the repeating quantity may vary. The texture may be a color or albedo variation as in a checkerboard, a paisley print or zebra stripes. Very often in real-world scenes, texture is instead due to a surface height variation, e.g. pebbles, gravel, foliage and any rough surface. Such surfaces are referred to here as 3D textured surfaces. Standard texture recognition algorithms are not appropriate for 3D textured surfaces because the appearance of these surfaces changes in a complex manner with viewing direction and illumination direction. Recent methods have been developed for recognition of 3D textured surfaces using a database of surfaces observed under varied imaging parameters. One of these methods is based on 3D textons obtained using K-means clustering of multiscale feature vectors. Another method uses eigen-analysis originally developed for appearance-based object recognition. In this work we develop a hybrid approach that employs both feature grouping and dimensionality reduction. The method is tested using the Columbia-Utrecht texture database and provides excellent recognition rates. The method is compared with existing recognition methods for 3D textured surfaces. A direct comparison is facilitated by empirical recognition rates from the same texture data set. The current method has key advantages over existing methods including requiring less prior information on both the training and novel images.