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We present a fingerprint classification algorithm in this paper. This algorithm classifies a fingerprint image into one of the five classes: arch, left loop, right loop, whorl, and tented arch. We use a new low-dimensional feature vector obtained from the output of a novel oriented line detector. Our line detector is a co-operative dynamical system that gives oriented lines and preserves multiple orientations at points where differently oriented lines meet. Our feature extraction process is based on characterizing the distribution of orientations around the fingerprint. We discuss three different classifiers: support vector machines, nearest-neighbor classifier, and neural network classifier. We present results obtained on a National Institute of Standards and Technology (NIST) fingerprint database and compare with other published results on NIST databases. All our classifiers perform equally well, and this suggests that our novel line detection and feature extraction process indeed captures all the crucial information needed for classification in this problem.