The fusion of multi-sensor data is dependent upon a paradigm of evidence accumulation. Many methodologies presently exist to carry out this fusion process, but the advantages and problems involved with each are not well documented or quantified. A modeling tool has been developed for these processes. This tool allows a comparison of evidence accumulation algorithms such as Bayes Nets and a Fuzzy Set concept. The tool allows variation of the number of sets of evidence accumulated as well as the correlation between these sets. These sets are compared by using the same inputs which can be interpreted as either features, decisions, or evidence. The performance differences between the paradigms are measured. A baseline fusion process is developed which takes into account the full covariance matrix of the data, thus taking into account the correlation between the evidence sources. This problem is completely solved and compared to the solution of the methods which implicitly or explicitly make assumptions about the interrelationship of data sources, methods such as Bayes, Dempster-Shafer, and Fuzzy Sets. The results are given in parametric form which can be utilized to develop design decision tools for systems. This tool will allow comparisons of methods and measures of bounding errors using the paradigms based on assumptions. An outline is given as to the assumptions required for each of the methods and how these impact the type of data required. The results of a sensitivity analysis, which shows the significance of these results in evaluation methodologies, is discussed.