Real-time classification of objects from active sonar echo-location requires a tremendous amount of computation, yet bats and dolphins perform this task effortlessly. To bridge the gap between human-engineered and biosonar system performance, we developed special-purpose hardware tailored to the parallel distributed nature of the computation performed in biology. The implemented architecture contains a cochlear filterbank front-end performing time-frequency feature extraction, and a kernel-based neural classifier for object detection. Based on analog programmable components, the front-end can be configured as a parallel or cascaded bandpass filterbank of up to 34 channels spanning the 10 to 150 kHz range. The classifier is implemented with the Kerneltron, a massively parallel mixed-signal Support Vector "Machine" in silicon delivering a throughput in excess of a trillion (½¼ ½¾ ) multiplyaccumulates per second for every Watt of power dissipation. The system has been evaluated on detection of mine-like objects using linear frequency modulation active sonar data (LFM2, CSS Panamy City), achieving an out-of-sample performance of 93% correct single-ping detection at 5% false positives, and a real-time throughput of 250 pings per second.