This study presents an exploration of several machine learning and image processing theories, as well as a literature review of several previous works on concrete crack detection systems. Through the literature review a system is selected and implemented with the Öresund bridge photograph collection. The selected system is a Convolutional Neural Network (CNN) using cropped (256x256x) images for input. The CNN has a total of 13 layers that were implemented as described in the paper. All parts of the implementation such as cropping, code, and testing are described in detail. This study found a final accuracy rate of 77% for the trained net. This is combined with a sliding window technique for handling larger images. An exploration of reasons for this accuracy rate is done at the end of the paper.