This thesis describes an automatic computer vision system for color identification. The system deals with color objects, such as wooden parts, that exhibit large texture variations and subtle color differences. In recent years, color has been used more and more as an important cue for recognizing textured objects. Many proposed methods use color histograms as representations of color images. Most of these methods depend on proper quantization of the color space. In our system, a novel adaptive color space quantization scheme has been employed. The method is class-oriented and is integrated with a supervised training algorithm. From a set of training samples, a partition of the original RGB color space is determined, based on the intersection of meaningful parametric descriptions of the classes. Color histograms are constructed relative to the resulting partition of the color space, and serve as the representations of both the test images and the models in the database. Relative entropy, an information-theoretic similarity measure, has been used to perform the recognition. The system described in this thesis has been extensively tested in the laboratory and has shown a high recognition accuracy.