Echoes from four different cylinders having the same outer diameter (3.8 cm) and length (10.2 cm) were collected at 1-deg increments from the broadside to the end aspect and used in a neural network recognition study. The water-filled stainless-steel, water-filled aluminum, and foam-filled aluminum cylinders had the same 0.64-cm wall thickness. The fourth cylinder consisted of coral rock pebbles embedded in degassed epoxy. A dolphinlike sonar signal with a peak frequency of 120 kHz and a duration of approximation 50 (mu)s was used in a monostatic echo collection system. Fifty echoes digitized at a 1-MHz rate were collected for each target at each aspect. Time-frequency representation of the echoes along with the envelope of the matched filter response were fed to two parallel independent multilayered feedforward backpropagation neural networks and the results were fused together in another feedforward network. The network was able to achieve better than 90% correct recognition of the echoes when trained with echoes that were separated in increments of 4 deg. Performance dropped to approximately 80% when the network was trained on echoes separated in increments of 10 deg.