Abstract: The method based on feature prediction and error mapping is used to conduct multi-static target identification. The target identification conditional probability is obtained through the Bayes rule. And the formula equation is simplified. The prediction feature of the last receiver is obtained by BP(Back Propagation) neural network, and the prediction error probabilistic is calculated using the Gaussian mixture model. The improved scheme expands the method using feature prediction and error mapping to calculate the conditional probability of multi sonar nodes and replaces the single Gaussian model with the Gaussian mixture model to solve the problem of inaccuracy of error probability distribution model. Then the multiplied result by the identification probability of each former receiver is used to obtain the conditional probability of the target type. The procedure is repeated for every target, and the target type corresponding to the maximum probability is the type of target to be identified. Multi-static simulation experiment is conducted in anechoic tank, and the above method is employed to identify four types of targets. In the condition of certain sonar numbers and signal-to-noise ratio, compared with mono-static sonar system, after employing the multi-static fusion recognition method, the identification rate of multi-static sonar system is increased by 40% and the identification rate is increased more after the method is improved.