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A method for temporal pattern recognition for continuous time signals is addressed. It is shown how a simple form of back-propagation can be used in conjunction with a temporal error signal to adapt both the weights and path delays of a continuous time delay feed forward multi-layer neural network with hard-limited output. An instance of such a network is simulated and some of the results are discussed. During the initial tests the network showed robust capabilities for detection of temporal patterns, including fast recognition of onsets of new waveforms in presence of moderately heavy noise and phase and frequency distortions.