To investigate necessary conditions for the object recognition by simulations using neural network models is one of ways to acquire suggestions for understanding the neuronal representation of objects in the brain. In the present study, we trained a three layered neural network to form a geometrical feature representation in its output layer using back-propagation algorithm. After training using 73 learning examples, 65 testing patterns made by various combinations of above features could be recognized with the network at a rate of 95.3% appropriate response. We could classify four types of hidden layer units on the basis of effects on the output layer.

We reported that a competitive learning neural network had the ability of self-organization in the classification of questionnaire survey data. In this letter, its self-organized learning was evaluated by means of mutual information. Mutual information may be useful to find efficently the network which can give optimal classification.