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Nowadays, biometrics is a research field in full expansion, several identification and verification systems are now developed, however their performances remain unsatisfactory facing to the growing security needs. Generally, the use of only one biometric decreases the reliability of these systems; thus, we have to combine several modalities. In this paper, we propose a multibiometric fusion approach for identity verification using two modalities: the fingerprints and the signature. Combinations of neural multi-layer perceptrons (MLP) are used for the unimodal classification. Our multimodal integration approach is based on the use of Support Vector Machines (SVM). The final identity verification decision is made according to the scores generated by the SVM classifier. The experimental results of the proposed multibiometric system are encouraging.