This concerns the design, tratilng, test and evaluation of a feed-forward neural network for classifying acoustic signals emitted by ships in transit by an omnidirectional hydrophore. Relatively noisy surface ships, moving rapidly at medium to long range, emit signals which superficially resemble those of quieter submarines, moving more slowly and closer to the listening device. The neural network approach is motivated by an obvious analogy to the sonar classifier of German and Sejnowski, who trained a neural network to classify active sonar returns from two undersea objects. The present problem can be solved by a similar network architecture, the outputs indicating which target type (if any) is present. The inputs represent the evolution of spectral densities for each of a number of time lags. Yet the number of target types and encounter geometries is far greater than could possibly be covered in any representative way by a training set comprised of real world data, Thus the task is to connect the network to a high fidelity, model-based digital simulator and to show that, by training on the output of the simulator, the neural network can learn to pass realistic tests. Permission to copy without fee all or part of this material is gmrtted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the pubtieation and its date appear, and notice is given that copying is by permission of the Association for Computing Mach~:ry. TOCOPY otherwise,or to republish requires a fee andlor spectilc perrms sion. This describes a neural network design-and-testing exercise based on a simplistic model that captures a few of the salient features of the problem.