In a neural network, input neuron units of an input layer are grouped into first through J-th input layer frames, where J represents a predetermined natural number. Intermediate neuron units of an intermediate layer are grouped into first through J-th intermediate layer frames. An output layer comprises an output neuron unit. Each intermediate neuron unit of a j-th intermediate layer frame is connected to the input neuron units of j'-th input layer frames, where j is variable between 1 and j and j' represents at least two consecutive integers, one of which is equal to j and at least one other of which is less than j. Each output neuron unit is connected to the intermediate neuron units of the intermediate layer. For recognition of an input pattern represented by a time sequence of feature vectors, each consisting of K vector components, where K represents a predetermined positive integer, each input layer frame consists of K input neuron units. Each intermediate layer frame consists of M intermediate neuron units, where M represents a positive integer which is less than K. The vector components of each feature vector are supplied to the respective input neuron units of one of the input layer frames that is preferably selected from three consecutively numbered input layer frames. The neural network is readily trained to make a predetermined one of the output neuron units produce an output signal indicative of the input pattern and can be implemented by a microprocessor.