Condition monitoring of rolling element bearings is crucial for ensuring the reliability and longevity of rotating machinery in various industrial applications. This paper presents a comprehensive study on the application of artificial neural networks (ANNs) for the effective condition monitoring of rolling element bearings. The research aims to develop an advanced predictive maintenance framework that leverages the capabilities of ANNs to identify and classify bearing faults with high accuracy. The study begins with extensive experiments are conducted to acquire bearing vibration signal under various operational scenarios. The experimental setup involved a test rig equipped with accelerometers and data acquisition systems to capture high-fidelity vibration signals. These signals were preprocessed and then fed into the neural network for training purposes. ANN architectures are more effective model for fault diagnosis to identify operation defects. The evaluation metrics used included accuracy, precision, recall, and F1 score to provide a comprehensive assessment of the model’s diagnostic capabilities. The results demonstrated that the ANN-based approach significantly outperformed traditional methods, achieving higher accuracy and robustness in fault classification, and the experimental results shows progression of defects on the bearing surfaces over a time. Additionally, the ANN models exhibited strong generalization capabilities, maintaining high performance across different bearing types and operating conditions.