Abstract: Because the parameters of the performance of a dynamically tuned gyroscope has degradation,we propose a method for predicting its faults,which combines the self-organizational feature mapping(SOFM) neural network with the hidden Markov model(HMM).Firstly,we gather the vibration,temperature,random drifting,motor power,power source voltage,frequency and other signals of the dynamically tuned gyroscope as the feature information for charactering its degradation,and then use the SOFM neural network to implement the multi-sensor information fusion.Secondly,we use the HMM to transform the early fault signals,which are difficult to detect,into the easily observed information,thus predicting the faults of the dynamically tuned gyroscope.The experimental results show that our method can easily and effectively extract the feature information on the gyroscope's degradation.The training and testing with the HMM show that our method is effective for fault predictions.