Alternative paradigms for fusion of a mix of information at the data, feature, and decision levels, acquired from multiple sources (sensors as well as feature extractors and/or decision processors) are explored in this study. Four alternative approaches—a self-partitioning neural net, an adaptive fusion process, an evidential reasoning approach, and a concurrence-seeking approach, were selected for consideration and relatively evaluated from a conceptual viewpoint followed by some limited simulation and testing. As a result of this relative assessment, an adaptive fusion processor, based on certain innovative advances of the nearest-neighbor concept which were developed previously in the context of automatic target recognition (ATR) designed for operation in partially exposed environments, was selected for detailed implementation and testing. Details of this adaptive fusion processor are presented here along with examples of tests conducted using some real-world field data which clearly bring out the feasibility and effectiveness of the new multilevel fusion concepts. The results show the benefits of fusion in terms of improved performance compared with those obtainable from the individual component information streams being input to the fusion processor.