Multi-Sensor Fusion is founded on the principle that combining information from different sensors will enable a better under-standing of the surroundings. However, it would be desirable to evaluate how much one gains by combining different sensors in a fusion system, even before implementing it. This paper presents a methodology and tool that allows a user to evaluate the classification performance of a multi-sensor fusion system modeled by a Bayesian network. Specifically, we first define a generic global confusion matrix (GCM) to represent classification performance in a multi-sensor environment, we then develop a methodology with analytical convergence bounds to estimate the performance. The resulting system is designed to answer questions such as: (i) What is the probability of correct classification of a given target using a specific sensor individually? (ii) What if a specific set of sensors combined together are used instead? (iii) What is the performance gain by adding another sensor to this set? and (iv) Which sensors provide a better cost/benefit ratio? These questions are answered based on the probability of correct classification that can be analytically estimated using Bayesian inference with the given sensor models defined by confusion matrices. The principle that combining information enhances the understanding of the surroundings is also supported by the analysis made in example models for air target tracking and classification using the developed tool.