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This paper introduces a system for landmine detection using the sensor data generated by a ground penetrating radar (GPR). The GPR produces a three-dimensional array of intensity values, representing a volume below the surface of the ground. First, a constant false alarm rate (CFAR) detector is used to focus the attention and identify the candidates that resemble mines. Next, we apply a feature extraction algorithm based on projecting the data onto the dominant eigenvectors in the training data. The training signatures are then clustered to identify a few representatives, and a fuzzy k-nearest neighbor rule is used to distinguish true detections from false alarms.