提出了一种将主成分分析法(PCA)和径向基神经网络(RBF)算法相结合的手指静脉分类算法，即PCA-RBF算法。首先对手指静脉训练样本进行PCA降维，提取图像主要成分，利用RBF神经网络分类识别中的优势，对降维后的静脉图像分类，并采用最短距离法进行识别，通过与BP神经网络识别效果的对比试验，结果表明，PCA-RBF方法加快了手指静脉识别的训练速度、简化了算法结构、提高了识别率。 This paper proposes a finger vein classification algorithm which combines Principal Component Analysis (PCA) with Radial Basis Function (RBF) neural network algorithm, named the PCA-RBF algorithm. Use the training sample to reduce PCA dimensions, and abstract the main component of the image. Because of the advantages of RBF neural network classifying, put finger vein images into different classes, and then use the shortest distance to recognize. Through the experiment result comparing with Back Propagation (BP) neural network, PCA-RBF neural network is better in finger vein recognition. The result shows that PCA-RBF has faster training speed, simpler algorithm and high- er recognition rate.