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Large scale, automatic fingerprint identification systems (AFISs) perform fingerprint classification to improve matching accuracy and reduce the matching time before fingerprint matching. Fingerprints are classified into several classes such as arch (A), whorl (W), left loop (L) and right loop (L). The existing systems generally classify fingerprints based on the information of singular points. This approach is well suited for fingerprints acquired using paper and ink. However, it is not as efficient with recent automatic fingerprint systems because it cannot guarantee that singular points are well extracted since the recent systems have various sized sensors and use multifarious fingerprint acquisition methods. In this paper, a novel approach is proposed to use the fingerprint ridge direction, which is one of the global features. It is a probabilistic approach based on the fingerprint ridge characteristics of each class. FVC2000 DB1 and FVC2002 DB1 databases were used to evaluate the performance of our classification. Furthermore, the effectiveness of applying the probabilistic model to the classification of various exceptional fingerprint patterns was verified.