In this paper we investigate the problem of fast and accurate classification of high range resolution radar returns. In addition we investigate the problem of efficient organization of large databases of pulsed high resolution radar returns from naval vessels in order to economize memory requirements and minimize search time. We use both synthetic radar returns from ships as well as real ISAR returns as the experimental data. We develop a novel algorithm for hierarchically organizing the database, which utilizes a multiresolution wavelet representation working in synergy with a Tree Structured Vector Quantizer (TSVQ), utilized in its clustering mode. The tree structure is induced by the multiresolution decomposition of the pulses. The TSVQ design algorithm is of the 'greedy' type. We demonstrate that our algorithm automatically computes the aspect graph (i.e. the simultaneous representation of compressed pulses as functions of aspect and elevation) for single target or for a group of targets. We also develop a novel optimization framework for the simultaneous design of the wavelet basis, the Tree-Structured Vector Quantizer and the Classification rule. We show that an efficient implementation consists of an adaptive Wavelet Transform - Tree Structured Vector Quantization with Learning. We show experimental results on the performance of the algorithm as measured by: (a) memory requirements; (b) search time; (c) scaling with respect to size; (d) accuracy in recovery. We also show experimental results with respect to variations in the mother wavelet and the design of the tree, as well as their impact on the performance of the algorithm. The results indicate that the combined algorithm results in orders of magnitude faster data search time, with negligible performance degradation (as measured by rate-distortion curves).