Information gathered by different knowledge sources from the same scene are often uncertain, imprecise, fuzzy, vague, or incomplete. Numerous papers have appeared in the literature dealing with the fusion of this kind of information using different frameworks. In this paper, we review a number of non-deterministic methods for solving the fusion problem. The use of Bayes’ rules in resolving ambiguities and conflicts associated with given bodies of evidence is examined. We also present the theory of belief (i.e., Dempster’s rule of combination) and its use in evidence fusion. The theory of possibility, which has emerged from the theory of fuzzy sets, the sym metric sums, and other hybrid’ techniques are also examined. A meaningful comparison among all these methods is carried out using the same set of synthetic data presented in various frameworks. This example is inspired from a real robotic experiment. The strengths and weaknesses of these techniques are discussed in some detail. Based upon the performance of each method on this particular fusion problem, general comparative remarks are given.