State-of-the-art in multi-sensor (MSI) application involves extensive research and development time to understand and characterize the application domain; to determine and define the appropriate sensor suite; to analyze, characterize, and calibrate the individual sensor systems; to recognize and accommodate the various sensor interactions; and to develop and optimize robust merging code. Much of this process can benefit from adaptive learning, i.e., an output-based system can take raw sensor data and desired merged results as input and adaptively develop an effective method of interpretation and merger. This approach significantly reduces the time required to apply MSI to a given application, while increasing the quality of the final result and provides a quantitative measure for comparing competing MSI techniques and sensor suites. The ability to automatically develop and optimize MSI techniques for new sensor suites and operating environments makes this approach well suited to the detection of mines and mine-like targets. Perhaps more than any other, this application domain is characterized by innovative and dynamic sensor suites, while nature and interactions are not yet well established. This paper presents such an outcome-based multi-image analysis system. An empirical evaluation of its performance, application, and sensor and domain robustness is presented.