A classification method to distinguish the neonatal pain expression from non-pain expression is proposed, which combines Gabor wavelet transform with support vector machine (SVM). At first, each neonatal facial image, which is normalized to the size of 112 pixel×92 pixel, is transformed by the 2D Gabor wavelet to extract 412160 Gabor features. Since the high-dimensional Gabor feature vectors are quite redundant, AdaBoost is introduced as a feature selection tool to remove the redundant ones. In experiments, 900 features are selected from 412160 original Gabor features. Finally, the selected Gabor features are fed into the SVM for final classification. This method takes the advantages of the favorable ability of Gabor feature in representing facial expression, the effective function of Adaboost in feature selection, and the high performance of SVM in the solution to small sample size, high dimension problems. Experiments with 510 neonatal expression images show that the method is quite effective. The best recognition rates of pain versus non-pain (85.29%), pain versus calm (94.24%), pain versus cry (78.24%) are obtained.