For the automatic discrimination of characteristic events using telemetry instruction parameters, the existing methods ignore the condition of mutual verification between data sources, and only judge the occurrence time of characteristic events based on the same measured values, which has a certain misjudgment probability. A discriminant method of characteristic events based on Bayesian estimation is proposed. The method not only takes advantage of the current measured value but also takes into account the inherent prior distribution information of characteristic events, which can effectively improve the accuracy of automatic discrimination. Firstly, by analyzing the connotation of data sources for characteristic events, the conditions of mutual corroboration of diffe-rent data sources are obtained. Firstly, through the analysis of the connotation of characteristic event data sources, the conditions of mutual verification between data sources are obtained. Secondly, through the statistical analysis of the historical data, the normal distribution probability model of the occurrence time for characteristic events is established. The algorithm based on Bayesian maximum posterior estimation is proposed, and the discriminant method of characteristic events is designed. Finally, through the simulation calculation of engineering data and the analysis of results, the effectiveness and practicability of this method are verified.