Since the last decade, gearbox systems have been requiring increasing power, and consequently, the complexity of systems has escalated. Inevitably, this complexity has resulted in the need for the troubleshooting of gearbox systems. With a growing trend of health monitoring in rotating machines, diagnostic and prognostic studies have become focused on diagnosing existing and potential failures in gearbox systems. In this context, this study develops the architecture of the cloud-based cyber-physical system (CPS) for condition monitoring of gearbox. Empirically collected vibration signals of gear wear at various time intervals are processed using empirical mode decomposition (EMD) algorithm. A Euclidian-based distance evaluation technique is applied to select the most sensitive features of car gear wear. Artificial neural network (ANN) is trained using extracted features to monitor the gearbox for the future dataset. Comparison of the performance results revealed that the ANN is superior to the other EMD methods. The present methodology was found efficient and reliable for condition monitoring of industrial gearbox.