Support Vector Machines (SVMs) are an emerging machine learning technique that has found widespread application in various areas during the past four years. The success of SVMs is mainly due to a number of attractive features, including a) applicability to the processing of high dimensional data, b) ability to achieve a global optimum, and c) the ability to deal with nonlinear data. One potential application for SVMs is High Range Resolution (HRR) radar signatures, typically used for HRR-based Automatic Target Recognition (ATR). HRR signatures are problematic for many traditional ATR algorithms because of the unique characteristics of HRR signatures. For example, HRR signatures are generally high dimensional, linearly inseparable, and extremely sensitive to aspect changes. In this paper we demonstrate that SVMs are a promising alternative in dealing with the challenges of HRR signatures. The studies presented in this paper represent an initial attempt at applying SVMs to HRR data. The most straightforward application of SVMs to HRR-based ATR is to use SVMs as classifiers. We experimentally compare the performance of SVM-based classifiers with several conventional classifiers, such as k-Nearest-Neighbor (kNN) classifiers and Artificial Neural Network (ANN) Classifiers. Experimental results suggest that SVM classifiers possess a number of advantages. For example, a) applying SVM classifiers to HRR data requires little prior knowledge of the target data, b) SVM classifiers require much less computation than kNN classifiers during testing, and c) the structure of a trained SVM classifier can reveal a number of important properties of the target data.