For use in recognizing an input pattern consisting of feature vectors positioned at a first, ,,,, an i-th, ..., and an I-th pattern time instant along a pattern time axis, a connected word recognition system comprises first through N-th neural networks B(1) to B(N) which are assigned to reference words identified by a first,..., an n-th, ..., and an N-th word identifier and are arranged along a signal time axis divisible into a first, ..., a j-th, ..., and a J-th signal time instant. The n-th word identifier n corresponds to consecutive ones of the pattern time instants by a first function n(i). The time axes are related to each other by a second function j(i). Among various loci (n(i), j(i)) in a space defined by the word identifiers and the time axes, an optimum locus (n/< ANd >(i), j/< ANd >(i)) is determined to maximize a summation of output signals of the respective neural networks when the consecutive pattern time instants are varied between the first and the I-th pattern time instants for each word identifier. The input pattern is recognized as a concatenation of word identifiers used as optimum first functions n/< ANd >(i) in the optimum locus. Preferably, the optimum locus is determined by a dynamic programming algorithm. If possible, use of a finite-state automaton is more preferred.
Connected word recognition system including neural networks arranged along a signal time axis.