Improvements in the design of small unmanned underwater vehicles and man's ever increasing exploitation of the oceans, insures that autonomous pattern recognition systems will play an increasingly significant role in undersea systems. Unmanned underwater missions are an attractive alternative to dangerous and costly manned missions. Of national interest are exploration and mapping, environmental monitoring and cleanup, military applications as well as commercial exploitation. High performance classifiers are the greatest unsolved technical challenge to fielding autonomous undersea systems. Classification of man-made underwater objects is particularly difficult because the harsh sensing environment produces low resolution, low signal to noise data. Traditional approaches use single networks. These networks have performance limitations which are due in part to the restricted feature set size they can accommodate. Single networks are therefore not likely to perform optimally under varied environmental conditions. For similar reasons they have a problem taking advantage of multi- sensor data. In this paper, hierarchical neural network architectures, which address the limitations of single network classifiers, are developed. One of these architectures, the sequential classifier, is applied against a difficult side scan sonar data set containing a highly cluttered and variable environment. Performance of the sequential classifiers is compared to the traditional single network classifiers, whose feature sets have been optimized, and show significant improvement. Sequential classifiers have achieved a detection rate of .98 and a .002 false alarm rate. A subset of the networks have been demonstrated on special purpose hardware to run in real time.