In the present paper an automatic training procedure for adaptive neuro-fuzzy inference system (ANFIS) networks is presented. The initialization of the net is carried out by the /spl beta/-min-max fuzzy clustering procedure, which is a modified version of the original min-max technique by Simpson (1993). Parameter /spl beta/ affects the number, position and size of resulting clusters. Since different P values yield different initializations, the optimal one is chosen by applying a well known result of the learning theory, which states that, under the same condition of performance on training set, the net that shows the best generalization capability is the one which is characterized by the lowest structural complexity. An automatic backpropagation-like procedure is finally used to perform a fine tuning of the optimal net. Simulation tests and comparison with other non-automatic learning procedures are discussed.