In underwater sensing applications, it is often difficult to train a classifier in advance for all targets that may be seen during testing, due to the large number of targets that may be encountered. We therefore partition the training data into target classes, with each class characteristic of multiple targets that share similar scattering physics. In some cases, one may have a priori in-sight into which targets should constitute a given class, while in other cases this segmentation must be done autonomously based on the scattering data. For the latter case, we constitute the classes using an information-theoretic mapping criterion. Having defined the target classes, the second phase of our identification procedure involves determining those features that enhance the similarity between the targets in a given class. This is achieved by using a genetic algorithm (GA)-based feature-selection algorithm with a Kullback-Leibler (KL) cost function. The classifier employed is appropriate for multiaspect scattering data and is based on a hidden Markov model (HMM). The performance of the class-based classification algorithm is examined using both measured and computed acoustic scattering data from submerged elastic targets.