Air compressors are critical components in many industries whose catastrophic failure results in huge financial losses anddowntime leading to accidents. Hence, real time fault diagnosis of air compressor is essential to predict the health conditionof air compressor and plan scheduled maintenance thereby reducing financial losses and accidents. Fault diagnosis usingtransfer learning aids in real time fault detection. In the present study, five air compressor conditions were considerednamely, check valve fault, inlet and outlet reed valve fluttering fault, inlet reed valve fluttering fault, outlet reed valvefluttering fault, and good condition. The raw vibration data was converted to radar plot images that were pre-processed andclassified using four pre-trained networks (ResNet-50, GoogLeNet, AlexNet, and VGG-16). The hyperparameters likeepochs, batch size, optimizer, train-test split ratio, and learning rate were varied to find out the best network for aircompressor fault diagnosis. ResNet-50 among all other pre-trained networks produced the maximum classificationaccuracy (average of five trials) of 98.72%.