Multiple-look fusion is quickly becoming more important in statistical pattern recognition. With increased computing power and memory one can make many measurements on an object of interest using, for example, video imagery or radar. By obtaining more views of an object, a system can make decisions with lower missed detection and false alarm errors. There are many approaches for combining information from multiple looks and we mathematically compare and contrast the sequential probability ratio test, Bayesian fusion, and Dempster-Shafer theory of evidence. Using a consistent probabilistic framework we demonstrate the differences and similarities between the approaches and show results for an application in infrared video classification.