A novel approach for the fusion of heterogeneous object classification methods is proposed. In order to effectively integrate the outputs of multiple classifiers, the level of ambiguity in each individual classification score is estimated using the precision/recall relationship of the corresponding classifier. The main contribution of the proposed work is a novel fusion method, referred to as Dynamic Belief Fusion (DBF), which dynamically assigns probabilities to hypotheses (target, non-target, intermediate state (target or non-target) based on confidence levels in the classification results conditioned on the prior performance of individual classifiers. In DBF, a joint basic probability assignment, which is obtained from optimally fusing information from all classifiers, is determined by the Dempster's combination rule, and is easily reduced to a single fused classification score. Experiments on RSVP dataset demonstrates that the recognition accuracy of DBF is considerably greater than that of the conventional naive Bayesian fusion as well as individual classifiers used for the fusion.