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A fundamental problem in system modeling and theory is the characterization of the structure of an unknown nonlinear stochastic system when only input-output measurements are available. A method of classifying nonlinear stochastic systems, using pattern recognition and a pattern vector constructed from the input-output data, is proposed for ten stated classes of low-order nonlinear systems. The method is capable of extension to additional classes of nonlinear systems. Extensive experimental results are given to show that classification of an unknown nonlinear system, with respect to basic structural properties, can be and accomplished with a very high probability of correct classification. Various applications of the classification procedure are given, particularly in the areas of systems modeling, self-organizing control systems, and learning control systems.