Complex computer vision systems, such as those used for automatic target recognition, have a need to aggregate evidence from a variety of sources when making decisions. We present two complementary methodologies, based on the theory of fuzzy sets, to fuse partial support for a decision or hypothesis from a variety of sources into a final degree of confidence. The sources may include different sensors, pattern recognition algorithms, expert systems, features; evidence over time, intelligence data, etc. Both methodologies--the fuzzy integral and fuzzy aggregation networks--are powerful and flexible means of combining partial degrees of support into a final decision. The choice of technique is governed by the demands of the problem, availability and type of training data, and a priori knowledge of the situation. The fuzzy integral is a nonlinear functional which combines (possibly subjective) knowledge concerning the worth of subsets of sources, in the form of a fuzzy measure, with objective evidence supplied by the sources. Under certain conditions, the fuzzy measure are actually Dempster Shafer belief or plausibility measures. Means to automatically determine the measures from histograms of training data have been developed. Fuzzy aggregation networks are best utilized for hierarchical decision making where input/output training sets (much like those for neural networks) are available. Each node in the network is modeled as a general fuzzy set theoretic connective: union operator, intersection operator, generalized mean, or hybrid operator. The type of node, its parameters, and connection weights are learned during training. Both approaches have been applied to multisensor automatic target recognition problems with excellent results. They have also been successfully employed in image segmentation and general multicriteria decision making. Theoretical and practical issues, along with results, will be discussed.