This paper reflects on the use of the Artificial Neural Network ( ANN) approach to diagnose and interpret engine failure behaviour. The current research focuses on the analysis of quantitative wear trend patterns through Condition Tracking (CM) and soft computational approaches. Oil analysis has been carried out to observe the engine failure trend. An ANN model using a Nonlinear Autoregressive with Exogenous Input (NARX) architecture has been employed to predict quantitative outputs such as Wear Particle Concentration (WPC), Wear Severity Index (WSI), Severity Index (SI) and Percentage of Large Particle (PLP) in connection with input functions of Engine Running Hours, RPM and oil temperature. Correlation function and error similarity are statistically evaluated to represent the model's robustness and effectively chart the loss input-output sequence. The subsequent ANN model demonstrates the capabilities for advance diagnosis and better prediction of engine performance.