A quantitative model of human categorization behavior is proposed, which can be applied to 4-alternative forced-choice categorization data involving two binary classifications. A number of processing dependencies between the two classifications are explicitly formulated, such as the dependence of the location, orientation, and steepness of the class boundary for one classification on the outcome of the other classification. The significance of various types of dependencies can be tested statistically. Analyses of a data set from the literature shows that interesting dependencies in human speech recognition can be uncovered using the model.