This paper proposes a secure multimodal biometric recognition system with a multi-level fusion architecture. A multi-spectral cam-era is used to capture hand images in the visible and in the near-infrared (NIR) bands of the spectrum. The system uses four biome-tric traits from the user's hands: palmprint (PP), finger surface (FS), hand geometry (HG) and palm veins (PV), being the latter captured in the near-infrared band. In the feature extraction stage, three different techniques (i.e., Orthogonal Line Ordinal Features, Competitive Code and PalmCode) are implemented to extract fea-tures from the palmprint, finger surface and palm veins. The resulting features are then converted to binary in order to apply a secure template storage scheme, consisting of a cryptographic hash function combined with an error-correcting code. In the proposed system architecture, the hand geometry is used as a database indexing trait to reduce the search time needed for identification. Recognition results, obtained using a proprietary database that was built for that purpose, are presented for different combinations of the feature extraction techniques on the various biometric traits, as well as for different fusion methods.