Millimeter wave radar offers advantages in scene surveillance, traffic monitoring and health monitoring due to its penetrability and privacy. Abnormal human behaviors could be identified through the radar detection and classification process. In this paper, an abnormal human activity classification method based on micro-Doppler effect is proposed. The singular vector decomposition (SVD) and principle component analysis (PCA) are extracted from simulated radar echo and fed into a Generalized Regression Neural Network (GRNN) for classification.