Condition monitoring becomes an integral part of the industrial manufacturing system to ensure a safe working environment and reduce the cost of maintenance. Involving deep learning techniques in fault diagnosis methods not only increases the accuracy and reliability of the system but also reduces the operation time and hassle of the manual feature extraction process. In this paper, a complete framework for fault classification is introduced by using the vibration signals of bearings containing normal and faulty conditions. Firstly, the frequency spectrums of the time-series signals are generated with FFT and transformed the 1-D signal into 2-D images with the recurrence plots (RP) algorithm. Finally, a deep CNN model is designed to classify the bearing conditions with the extracted high-level features from the RP-based image dataset. The images show a distinct pattern in every bearing condition and the CNN model can achieve 99.24% accuracy to classify three different bearing conditions. The image classification-based fault diagnosis approach is automated and eliminates the disadvantages of the manual feature extraction process. The generated images with RP were also trained with three predefined CNN models to verify the effectiveness of the fault patterns. Finally, the comparative analysis demonstrates that the proposed method outperforms other researchers’ approaches both in terms of classification accuracy and computational cost.