PURPOSE: To reduce the number of neuro-elements in a hidden layer by applying a general method for providing the hidden layer of a three-layer type neural network with a linear separation function for pattern recognition.

CONSTITUTION: The three-layer neural network consists of S, H and R layers. Teacher signals are hi, lw. In one H layer neuro-element, several patterns having the teacher signal hi and capable of being linearly separated out of input patterns are separated from other patterns and a connection coefficient is set up by learning so that the former patterns output the signal hi and the latter ones output the signal lw. If patterns having the teacher signal hi are included in the residual input patterns, another H layer neuro-element is added, patterns to be linearly separated out of the residual input patterns having the teacher signal hi are separated from all other patterns and learning is furthermore repeated.