Abstract: This paper developed a multiclass fault diagnosis method for aero-engine,which used three-layer perceptrons(feed-forward neural networks) as weak classifiers and then they were combined together to create an aggregate hypothesis with AdaBoost algorithm.A final fault classifier is the neural network ensemble which was formed by the simply trained three-layer perceptrons and this made it possible to design practical neural network fault classifiers easily.A simulation experiment for the gas path components of a turbojet engine was conducted to demonstrate the effectiveness of the method.Through the experiment,24 groups of fault-test data of the turbojet engine were all correctly classified into 5 classes.The experimental results show that the generalization ability of the final fault classifier is improved effectively.