Dempster-Shafer evidential theory, a probability-based data fusion classification algorithm, is useful when the sensors (or more generally, the information sources) contributing information cannot associate a 100-percent probability of certainty to their output decisions. The algorithm captures and combines whatever certainty exists in the object-discrimination capability of the sensors. Knowledge from multiple sensors about events (called propositions) is combined using Dempster's rule to find the intersection or conjunction of the propositions and their associated probabilities. When the intersection of the propositions reported by the sensors is an empty set, Dempster's rule redistributes the conflicting probability to the nonempty set elements. When the conflicting probability becomes large, application of Dempster's rule can lead to counterintuitive conclusions. Several modifications to the original Dempster-Shafer theory have been proposed to accommodate these situations.