This paper presents a novel approach for personal identification based on a wavelet-based fingerprint retrieval system which encompasses three image retrieval tasks, namely, feature extraction, similarity measurement, and feature indexing. We propose the use of different types of Wavelets for representing and describing the textural information presented in fingerprint images in a compact way. For that purposes, the feature vectors used to characterize the fingerprints are obtained by computing the mean and the standard deviation of the decomposed images in the wavelet domain. These feature vectors are used both to retrieve the most similar fingerprints, given a query image, and their indexation is used to reduce the search spaces of candidate images. The different types of Wavelets used in our study include: Gabor wavelets, tree-structured wavelet decomposition using both orthogonal and bi-orthogonal filter banks, as well as the steerable wavelets. To evaluate the retrieval accuracy of the proposed approach, a total number of eight different data sets were considered. We also took into account different combinations of the above wavelets with six similarity measures. The results show that the Gabor wavelets combined with the Square Chord similarity measure achieves the best retrieval effectiveness.