Due to constant loads, gear wear, and harsh working conditions, gearboxes are subject to fault occurrences. Faults in the gearboxcan cause damage to the engine components, create unnecessary noise, degrade efficiency, and impact power transfer. Hence, thedetection of faults at an early stage is highly necessary. In this work, an effort was made to use transfer learning to identify gearfailures under five gear conditions—healthy condition, 25% defect, 50% defect, 75% defect, and 100% defect—and three loadconditions—no load, T1=9.6, and T2=13.3 Nm. Vibration signals were collected for various gear and load conditions using anaccelerometer mounted on the casing of the gearbox. The load was applied using an eddy current dynamometer on the output shaftof the engine. The obtained vibration signals were processed and stored as vibration radar plots. Residual network (ResNet)-50,GoogLenet, Visual Geometry Group 16 (VGG-16), and AlexNet were the network models used for transfer learning in this study.Hyperparameters, including learning rate, optimizer, train-test split ratio, batch size, and epochs, were varied in order to achievethe highest classification accuracy for each pretrained network. From the results obtained, VGG-16 pretrained network outperformed all other networks with a classification accuracy of 100%.