We consider the problem of segmenting multitextured images using multiple Gabor filters. In particular, we present a mathematical framework for a multichannel texture-segmentation system consisting of a parallel bank of filter channels, a vector classifier stage, and a postprocessing stage. The framework establishes mathematical relationships between the predicted texture-segmentation error, the frequency spectra of constituent textures, and the parameters of the filter channels. The framework also enables the systematic formulation of filter-design procedures and provides predicted vector output statistics that are useful for classifier design. We focus on the mathematical framework and provide experimental results that confirm the utility of the framework in the design of a complete image-segmentation system. The results demonstrate effective segmentation using a straightforward classifier and fewer than half the number of filters needed in previously proposed approaches.