A new automatic target recognition algorithm to recognize and distinguish three classes of targets: personnel, wheeled vehicles and animals, is proposed using a low-resolution ground surveillance pulse Doppler radar. The Chirplet transformation, a time frequency signal processing technique, is implemented in this paper. The parameterized RADAR signal is then analyzed by the Zernike Moments (ZM) in order to feature extraction and they trained to the Nearest Neighbor classifier. The current work provides a new approach for multi-resolution analysis and classification of non stationary signals with the objective of revealing important features in noisy and cluttered environment. The algorithm is trained and tested on real RADAR signatures of multiple examples of moving targets from each class. Results show that the classifier is invariant to target aspect angle and speed.