Condition monitoring in the turbo machinery system is controlling parameters such as vibration domain and vibration frequency for fault diagnosis in the system. Condition monitoring is the main part of Predictive Maintenance. Rotors used in industrial machinery such as compressors, pumps, or turbines are often subjected to extreme loading during their operation. Rotating parts in machines exposed to external forces and temperatures may lead to a fatigue crack resulting in rotor damage. Structural health monitoring of the rotors is important for improving the safety of their operation and for extending their service life. All the usual faults like unbalance; misalignment etc. encountered in the rotor systems, the fatigue crack is the most dangerous one because if left undetected it can lead to catastrophic failure. This project describes the application of Wavelet Transform (WT), fuzzy logic and artificial Neural Network (ANN) for prediction of the faults effect on the frequency components of vibration signature in a shaft. These faults are unbalance, crack and combined faults of unbalance and crack. Existence of these faults in the shaft can lead to increase the damage in the system, if these faults not detected can increase the time and costs of repair. For studding these faults finite element solution and experimental test is used and vibration signals of shaft saved for 2 seconds. Continuous wavelet transform and scaled-averaged wavelet power method are used for signal processing. Then by conducting principal component analysis at these coefficients, they have been used as input of artificial neural network. Also total scaled-averaged wavelet power is used to identify system condition. The developed ANN is constructed of a hidden layer with 6 neurons and an output layer with 4 neurons. The network is trained with 40 sets of data relating to faulty shaft. In order to test the network, 30 sets of data relating to three fault states (unbalancing, crack and combined faults) obtained from numerical and experimental tests are used. The results show that the well-developed network has been able to detect system faults with the accuracy of 96.6%. Keywords: Condition monitoring, Wavelet, Fuzzy logic, Neural network