The nearest neighbor isomorphic network paradigm is a combination of sigma-pi units in the hidden layer and product units in the output layer. Good initial weights can be found through clustering of the input training vectors, and the network can be successfully trained via back propagation learning. We show theoretical conditions under which the product operation can replace the Min operation. Advantages to the product operation are summarized. Under some sufficient conditions, the product

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... ation yields the same classification result as the Min operation. We apply our algorithm to a geometric shape recognition problem and compare the performances with those of two other well-known algorithms.