Automatic License Plate Recognition (ALPR) has been a topic of research for many years now due to its real-life application but hasn’t been any significant breakthrough due to limitations in image processing algorithms to satisfy all the real-life scenarios such an illumination, moving cars, background etc. This paper presents a robust and efficient ALPR system using a combination of the ‘You only Look Once’ (YOLO) neural network architecture and standard Convolutional Neural Network (CNN). In total 3 stages of YOLO and 1 stage of CNN has been used in the proposed system. The last stage of YOLO and CNN have been specifically designed to perform detection (segmentation) and recognition of characters, respectively. We have built our own dataset of 604 car images in natural settings with different lighting conditions and viewing angles for the YOLO stages. In addition, a computer-generated dataset of 42237 characters has been used to train CNN. The resulting system has been tested on 50 random test images not part of training or validation datasets. The validation accuracies of all 4 stages exceed 90% whereas, the overall final accuracy on 50 test images comes to 82% with some fault tolerance. The use of deep learning instead of Image Processing also enabled to detect skewed license plates. The accuracy of stages 1 and 2 of YOLO were 100% on both validation and test sets.