Active sonar classification of suspended, bottomed, and buried mines is very important in littoral warfare. Current active sonars are inadequate because they require many emissions per potential target, yield high false alarm rates, and suffer from high clutter interference. New active biosonar models based on bat-like range profiling and dolphin-like image construction may reduce these problems. The performance of one such biosonar algorithm, the spectrogram correlation and transformation model developed at Brown University, has been compared with the performance of a standard matched filter on a data set obtained from NSWC Coastal Systems Station, Dahlgren Division. This data set contains echoes form six objects (two mine-like objects, a water-filled 50-gallon drum, a rough limestone rock, a smooth granite rock, and a water-saturated log). Three neural network architectures were used as classifiers. Discrimination was performed between man-made and non-man- made objects, between mine-like and non-mine-like objects, among the three types of man-made objects, and among the six different test objects using single pings, multiple ping fusion, fusion of the results from different algorithms, and a combination of algorithm fusion and multiple ping fusion.