We present new ÿngerprint classiÿcation algorithms based on two machine learning approaches: support vector machines (SVMs) and recursive neural networks (RNNs). RNNs are trained on a structured representation of the ÿngerprint image. They are also used to extract a set of distributed features of the ÿngerprint which can be integrated in the SVM. SVMs are combined with a new error-correcting code scheme. This approach has two main advantages: (a) It can tolerate the presence of ambiguous ÿngerprint images in the training set and (b) it can e ectively identify the most di cult ÿngerprint images in the test set. By rejecting these images the accuracy of the system improves signiÿcantly. We report experiments on the ÿngerprint database NIST-4. Our best classiÿcation accuracy is of 95.6 percent at 20 percent rejection rate and is obtained by training SVMs on both FingerCode and RNN-extracted features. This result indicates the beneÿt of integrating global and structured representations and suggests that SVMs are a promising approach for ÿngerprint classiÿcation.