This paper is concerned with an investigation of multispectral palmprint images for improving person identification by replying to the question: can multispectral palmprint images be reliable for such purpose? Two biometric systems are then proposed. In the first system, each spectral image is aligned and then used for feature extraction using 1D Log-Gabor filter. The features are encoded and Hamming distance is used for matching. The fusion at matching score level is used before the decision making. The second system is based on multiresolution analysis for feature extraction. The spectral images are decomposed into frequency sub-images with different levels of decomposition. The extracted coefficients are used as features. The MGPDF is used for modeling the features and Log-Likelihood scores are used for matching. Fusion at the matching score level is used before decision making. A comparative study between the two systems is then developed. The experimental results are demonstrated using the PolyU multispectral database and the results show that the two proposed systems are more effective when using multispectral images than their monospectral counterpart images.