For reasons of aviation safety and airport operations efficiency, gust front detection and tracking is an important product of Doppler weather radars developed for use in airport terminal areas. Previous gust front algorithms, which have relied on the detection of one or two conspicuous signatures in Doppler radar imagery, have worked reasonably well in images generated by the high-resolution, pencil-beam Terminal Doppler Weather Radar (TDWR). The latest Airport Surveillance Radar, enhanced with a Wind Shear Processor (ASR-9 WSP), is being developed as a less expensive alternative weather radar. Although gust fronts are visible to human observers in ASR-9 WSP imagery, the lower sensitivity and less reliable Doppler measurements of this radar make automated gust front detection a much more challenging problem. Using machine intelligence and knowledge-based signal processing techniques developed in the context of automatic target recognition, a Machine Intelligent Gust Front Algorithm (MIGFA) has been constructed that is radically different from the previous algorithms. Developed initially for use with ASR-9 WSP data, MIGFA substantially outperforms a state-of-the-art gust front detection algorithm based on earlier approaches. These results also indirectly suggest that MIGFA performance may be nearly as good as human performance. Preliminary results of an operational test period (two months, approximately 15000 scans processed) are presented.