19. A pattern recognition system comprising: a source for generating input patterns of multiple data types; a plurality of training subsystems, each training subsystem receiving a plurality of training input patterns of a single corresponding data type during a training operation, forming a set of categories of the training input patterns of the data type, generating a category definition for each category according to training input patterns received within the category, counting the number of training input patterns of each class within each category of the data type, and generating a training histogram for each category of the data type, each training histogram including counts of training input patterns of each class within the respective category; a plurality of classifiers, at least one classifier receiving during a testing operation at least one test input pattern of its corresponding data type, accessing the categories and computing a correlation between a category definition and each test input pattern, forming a category association between each test input pattern and a category based on the correlation and forming for each data type an observation histogram to classify the subject, the observation histogram being formed from each training histogram of each category of each category association and representing counts of training input patterns received by the training subsystem during the training operation; and a processor that combines each observation histogram for each data type into a cumulative histogram to form a cumulative classification of the subject within a class.
20. The pattern recognition system of claim 19 wherein at least one data type comprises visual data.