Much of the research in the area of multi-source fusion systems reported in the literature has concentrated on alternative methods of accomplishing fusion of information, such that the resulting fused output is in some sense better than any of the inputs individually by themselves. There is a wealth of literature expounding many variations on this theme of improving the information quality, reliability, or robustness through the use of a plethora of fusion concepts and tools. However, there has been little reported research aimed at providing an understanding of the causal relationship between the input information and the resulting fused output. This study explores this mostly virgin territory by conceptualizing fusion systems that are elucidative, i.e., systems that can, in some fashion, explain the results of the fusion process in terms of, for example, the relative influence of the different input information components (from the different sources) on the fused result. This new concept of elucidative fusion systems is illustrated in this study by inculcating such an elucidative property into a class of fusion systems operating on the principles of case-based reasoning. The potential for application to real-world problems is also demonstrated using the example of an audio-video system for recognition of spoken French vowels.