This paper presents the results of research into automatic recognition of a class of electronic communication signals using a Back Propagation (BP) model neural network. Communication signals present an important and interesting pattern recognition challenge since they change unpredictably over time in accordance with the information they carry. There are situations in which a receiver has no prior knowledge of a particular signal and must classify it before interpreting it. The communication systems of interest here use frequency division techniques to multiplex several telegraph sub-signals in a standard communication channel. Previous research in recognizing these signals has demonstrated good recognition rates at the cost of expensive signal preprocessing. In this research, a BP network, smaller than networks used previously on this problem, was trained to recognize several types of these signals with a high degree of accuracy using a feature vector that is computationally less expensive and smaller than previous feature vectors. The observation that the BP network is tolerant of noise in patterns is reaffirmed in this research.