Deteriorated performance data of a micro gas turbine were generated and the artificial neural network was applied to predict the deteriorated component characteristics. A program to simulate operation of a micro gas turbine was set up and deterioration of each component (compressor, turbine and recuperator) was modeled by changes in the component characteristic parameters such as compressor and turbine efficiency, their flow capacities and recuperator effectiveness and pressure drop. Single and double faults (degradation of single and two parameters) were simulated. The neural network was trained with a majority of the generated deterioration data. Then, the remaining data were used to check the predictability of the neural network. Given measurable performance parameters as inputs to the neural network, characteristic parameters of each component were predicted and compared with original data. The neural network produced sufficiently accurate prediction. Using a smaller number of input parameters decreased prediction accuracy. However, an acceptable accuracy was observed even without information on several input parameters.