Aiming at shift and rotation in fingerprint images, a novel dispersion degree and Ellipsoidal Basis Function Neural Network (EBFNN)-based fingerprint classification algorithm was proposed in this paper. Firstly, feature space was obtained through wavelet transform on fingerprint image. Then, the optimal feature combinations of different dimension were acquired by searching features in the feature space. And the feature vector was determined by studying the changes of divergence degree of those optimal feature combinations along with the dimensions. Finally, EBFNN was trained by the feature vector and fingerprint classification was accomplished. The experimental results on FVC2000 and FVC2002-DB1 show that the average classification accuracy is 91.45% if the number of the hidden neurons is 11. Moreover, the proposed algorithm is robust to shift and rotation in fingerprint images, thus it has some values in practice.