An event discrimination methodology executes multiple versions of the same or different event discrimination algorithms and logically or arithmetically combines their outputs to distinguish between specified events and non-events. One given algorithm is repeatedly executed with different sets of calibration data, or alternately, a number of different algorithms are executed. In cases where the algorithm results are arithmetically combined, the weights accorded to each algorithm result are dynamically adjusted based on driver input or vehicle dynamic behavior data to accord highest weight to the algorithm(s) calibrated to identify events associated with the detected driver input or vehicle dynamic behavior.