The project aims primarily to employ the benefits of artificial intelligence, specifically the characteristics of programming a neuronal network where neuronal networks, in turn, are networks that are interested in training and learning from error, and employing this error to achieve optimal results.Convolution NeuralNetworks(CNN)in particular are one of the most important neuronal networks that address classification problems and issues. Thus, this project is to design a convolution neuronal network that classifies vehicles into several types where we will design the network and train them on the database as the database includes pictures of several types of vehicles The network will classify each Image to its type, after adjusting the images, making the appropriate changes, turning them gray, and discovering the edges and lines.After the images are ready, the training process will begin, and after the training process is finished, we will produce classification results, and then we will test with a new set of images.One of the most important applications of this project is to abide by the paving places of cars, trucks, and vehicles in general, as if a picture was entered as a car for the car sample, which is a truck, for example, this will give an error where the network will discover this by examining and classifying it. As a truck, we discover that there is a violation of the paving laws