This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition 38.8 (2005): 1317 – 1319, DOI: 10.1016/j.patcog.2005.01.013 ; A novel score-level fusion strategy based on Bayesian adaptation for user-dependent multimodal biometric authentication is presented. In the proposed method, the fusion function is adapted for each user based on prior information extracted from a pool of users. Experimental results are reported using on-line signature and fingerprint verification subsystems on the MCYT real bimodal database. The proposed scheme outperforms both user-independent and user-dependent standard approaches. As compared to non-adapted user-dependent fusion, relative improvements of 80% and 55% are obtained for small and large training set sizes, respectively.