A method of on-the-fly training is presented that uses shape features to store representations of previously seen vehicles. Relationships between features are exploited such that recognition is possible over a range of relative sensor to target geometries, given a single or limited number of previously seen views. Initial results on SAR data has used zero crossings on filtered data, in addition to peak features, to perform adaptive matching. Using the AFRL ADAPTSAPS system, results for this adaptive approach are presented and discussed. Using a relatively limited number of previously seen samples of a target, the system under test in these experiments was able to start differentiating a selected target type from other targets and from confusers.