A steady increase in the number of vehicles on roads around the world has increased the need for Intelligent Traffic System (ITS). Vehicle detection, classification, and license plate recognition are essential for traffic analysis and ITS. License plate detectors are especially helpful to the law agencies, as they assist in catching criminals and suspicious vehicles by recognizing license plates. Systems employing Artificial Intelligence (AI) utilize image classification and object detection to monitor and analyze traffic on roads and highways. These systems are powered by state-of-the-art neural network architectures (e.g., the Convolutional Neural Network (CNN) for classification), which enable accurate detection and processing of real-time traffic data. Most vehicle monitoring systems, however, focus on only one aspect of vehicle tracking at a time. For example, these systems may focus on detecting specific attributes of a vehicle, such as vehicle’s make, model, or color, often ignoring the other attributes. This manuscript introduces a novel approach to vehicle monitoring systems, which involves focusing on all such attributes simultaneously. We divide the Stanford Cars Dataset (SCD) into groups based on the car’s make, model, type, year, and color. Subsequently, we train a separate CNN classifier on each group to learn the characteristics of each group. This allows us to study how each attribute independently affects the classification accuracy of vehicle monitoring system. Additionally, we also developed a license plate detector to complete our vehicle monitoring system. This provides a complete solution to the problem of vehicle monitoring systems by incorporating all essential components. Our vehicle recognition system achieves an average accuracy of 91.2% across all the different classification subtasks whereas the license plate detection system achieves an accuracy of 93.46%.