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Automated vehicle number plate recognition or Number Plate Recognition (NPR) is crucial in various applications, including law enforcement, traffic management, and toll collection systems. However, the traditional method of manual inspection of images is time-consuming and prone to errors. With the advancements in computer vision and deep learning techniques, identifying vehicle number plates has become more efficient and accurate. In this paper, we present a methodology for identifying vehicle number plates using the VGG16 convolutional neural network. We collected a dataset of vehicle images with their corresponding number plates and preprocessed the data to train the CNN using algorithms like transfer learning. The VGG16 CNN has been pre-trained on a large data set that is the ImageNet dataset, which makes it an excellent candidate for fine-tuning on specific tasks like vehicle number plate recognition. After training, we evaluated the performance of the CNN on a test dataset and deployed it in a real-world application. Our results show that the VGG16 CNN can accurately recognize number plates on different types of vehicles in various lighting conditions. This methodology improves efficiency and accuracy while reducing manual inspection errors. It can be used as an essential tool for automated number plate recognition in various applications, enabling faster processing times and reducing costs associated with manual inspection. The methodology presented in this paper demonstrates the effectiveness of deep learning techniques in automating vehicle number plate recognition. The VGG16 CNN architecture, combined with transfer learning and preprocessing techniques, can accurately recognize number plates, providing a reliable and efficient solution for automated number plate recognition system. [ABSTRACT FROM AUTHOR]