Abstract: Computers traditionally find it very hard to identify objects in images it is presented with, to solve this problem much research into machine learning has been produced since computers were invented. The goal of this paper is to introduce the idea of neural networks and investigate their effectiveness in recognising objects such as cars in a set of images.Tests were performed on a number of neural networks to obtain the best classifier and were then compared to classifiers produced by KNNand Classification Trees.Tests showed that a neural network was 92% accurate at identifying cars in a test set of images. This is an acceptable rate of recognition for multiple applications and was better than the result of the ClassificationTree which obtained only 81% accuracy. The network was however outperformed by the KNN classifier which obtained 98% accuracy. Regardless of this the neural network showed promise in computer vision, and has the capability to scale much better than the other classifiers tested here