One of the most critical problems in Automatic Target Recognition systems (ATR) is multi-scenario adaptation. Today's ATR systems perform unpredictably i.e perform well in certain scenarios, and they perform poorly in others. Unless ATR systems can be made adaptable, their utility in battlefield missions remain questionable. We have developed (under internal research and development) a novel concept called Knowledge and Model Based Algorithm Adaptation (KMBAA). KMBAA automatically adapts the ATR parameters as the scenario changes so that ATR can maintain optimum performance. The KMBAA approach has been tested with a non-real-time ATR simulation system and has demonstrated a significant improvement in detection, false alarm rate reduction and segmentation accuracy performance.