A complete mine detection/classification (D/C) system has been specified and implemented, which runs in real-time, and has been exercised on the latest available dual-frequency side-scan sonar acoustic image sets. The compete DC system is comprised of a collection of algorithms that has been developed and evolved at Draper Laboratory over the past decade. The detection process consists of image normalization, enhancement, segmentation, and feature extraction algorithms. The enhancement algorithm is a variant of a Markov Random Field based anomaly screener developed in FY-94. The feature that were extracted were those derived in FY-93. A distance constrained matching algorithm, which was developed in FY-95, is used to generate a list of high and low frequency fused tokens. The classification process involves the evaluation of a hierarchy of three multi-layer perceptron neural networks: HF, LF, and HF/LF fused. Research performed in FY-95 also concentrated on the development of several variants of information fusion with hierarchical neural networks. The 'discriminant-combining' variant of fusion was selected as part of this DC system. In addition, a classification post- processing and decision node statistic modification step, which was developed in FY-96, was included. This paper will describe the algorithm that were implemented. However, the emphasis will be on the performance results of processing the latest available side-scan imagery, comparison of single sensor vs dual-frequency sensor results, and the issues that were encountered while exercising the DC system on the new data set.