Fingerprint is those lines distributing on the finger surface. It's unique and stable. It has been hundreds years to use fingerprint to identity. AFIS (Auto Fingerprint Identification System) has been widely used. Besides traditional criminal, AFIS has been used to validate user in OS and web access, and embedded AFIS also has been used in ATM, credit card and door-lock. Fingerprint matching is one of the most important problems in AFIS. In general, the minutiae such as ridge endings and ridge bifurcation is to be used to represent a fingerprint and the fingerprint matching through minutiae matching. Based on this observation and by representing the minutiae as a point pattern, an automatic fingerprint verification problem may be reduced to a point pattern matching (minutia matching) problem. Point pattern matching is a famous problem in the field of pattern recognition. For a good point pattern matching approach, it is able to perform the geometrical invariant quantities (translation, rotation, and scaling) efficiently. Various algorithms have been proposed for point pattern matching. For example, the relaxation approach handles translational difference only and the complexity of triangles approach is very high. In this paper, an algorithm for fingerprint identification using point pattern matching based on cluster approach is proposed, which effectively solves the problems of optimal matching between two fingerprint minutiae images under geometrical transformation and minutiae quantity change. Process which bases on the matching of vector pairs is developed to determine the registration parameters. The experimental results show that the proposed matching algorithm is fast and has high accuracy.