We propose an adaptive multiresolution (MR) approach to the classification of fingerprint images. The system adds MR decomposition in front of a generic classifier consisting of feature computation and classification in each MR subspace, yielding local decisions, which are then combined into a global decision using a weighting algorithm. In our previous work on classification of protein subcellular location images, we showed that the space-frequency localized information in the MR subspaces adds significantly to the discriminative power of the system. Here, we go one step farther; We develop a new weighting method which allows for the discriminative power of each subband to be expressed and examined within each class. This, in turn, allows us to evaluate the importance of the information contained within a specific subband. Moreover, we develop a pruning procedure to eliminate the subbands that do not contain useful information. This leads to potential identification of the appropriate MR decomposition both on a per class basis and for a given dataset. With this new approach, we make the system adaptive, flexible as well as more accurate and efficient.