A prediction for about aeroengine vibration trend is proposed based on the process neural network. The analysis of vibration trend is influenced by many complicated factors during the practical operation period of aeroengines. It is difficult for the traditional methods to predict vibration change tendency effectively. Utilizing the polymerization effect and continuous input-output mapping of the system realized by nonlinear mapping capability to the time variable of process neural networks, the prediction method for aeroengine vibration trend had one input and output node, nine hidden nodes. Historical data are separated as learning samples and detection samples. The corresponding network model and learning algorithm are given. Under the same condition, compared with the traditional artificial neural network, the new network training speed improves and the prediction error to be decreased. Finally, the prediction method with the corresponding learning algorithm is used to predict the vibration trend of some aeroengines. Results are satisfactory.