Researchers have studied hybrid branch predictors that leverage the strengths of multiple stand-alone predictors. The common theme among the proposed techniques is a selection mechanism that chooses a prediction from among several component predictors. We make the observation that singling out one particular component predictor ignores the information of the non-selected components. We propose Branch Prediction Fusion, originally inspired by work in the machine learning field, which combines or fuses the information from all of the components to arrive at a final prediction. Our 32KB predictor achieves the same over-all prediction accuracy as the 188KB versions of the previous best performing predictors (the Multi-Hybrid and the global-local perceptron).