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An architecture of a surface acoustic wave (SAW) processor based on an artificial neural network is proposed for an automatic recognition of different types of digital passband modulation. Three feed-forward networks are trained to recognize filtered and unfiltered binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals, as well as unfiltered BPSK, QPSK, and 16 quadrature amplitude (16QAM) signals. Performance of the processor in the presence of additive white Gaussian noise (AWGN) is simulated. The influence of second-order effects in SAW devices, phase, and amplitude errors on the performance of the processor also is studied.