In order to solve the problem of palmprint recognition of a single sample,a new palmprint recognition method for a single sample based on the nearest correlation classifier(NCC) is developed.First,a palmprint image is partitioned into several smaller sub-images.Then the sub-image feature vectors are extracted by four feature extraction methods.They are statistics feature,Fourier transform,discrete cosine transform(DCT) and Gabor transform.The feature vectors of all the sub-images are combined together to form the feature vectors of the palmprint image.Finally,the pattern classification can be implemented by the NCC.The effectiveness of the developed approach is tested on the PolyU palmprint database.The experimental results show that compared with the nearest neighbor classifier(NNC) and the support vector machine(SVM),the recognition rates of the new classifier are significantly improved using different feature extraction algorithms in different size sub-images.The classification time is between 0.3 s and 0.7 s.Therefore the algorithm meets the real-time requirements.