Abstract: An interval ?0, 1! of the output of neural network is equally divided into M (M being an integer of two or more), and the numbers or frequencies of data for the correct/incorrect patterns contained in the i-th interval ?(i-1)/M, i/M! are .mu.1i and .mu.0i, respectively (where, i=1 . . . M). In this case, if this network provides an output contained in the i-th interval to unknown pattern data, this pattern is stored as a likelihood conversion table so that the pattern outputs likelihood P1i, which is a category, in an equation P1i=(.mu.1i+1)/(.mu.1i+.mu.0i+2). Then, when a value contained in the i-th interval ?(i-1)/M, i/M! is output from a neural network, the likelihood convertor receives it as an input and outputs P1i which is so to speak normalized likelihood.