Music recommendation systems have become popular in recent years with the increasing variety of music content being produced as well as the sheer size of digital music collections which are available at the touch of a finger. Large collections of digital music are commonly organized using genre labels. In addition, music genres are regularly used by recommendation systems to suggest new music to the listeners. The chore of classifying a large amount of music manually can be difficult and time consuming. It is for these reasons, the automatic classification of music by genre is a crucial task. The ability to automatically classify music by genre using machine learning can be quicker and arguably more accurate than doing it manually. Using neural networks for generic classification tasks is a well researched area within machine learning. In recent years, the classification of music by genre has become part of the same problem domain. Differences in song libraries, machine learning techniques, input formats, and types of neural networks implemented have all had varying levels of success. This thesis implements a convolutional neural network that classifies music by genre through the examination of spectrogram images. It concentrates on three specific types of spectrogram inputs (Linear, Logarithmic, and Mel scaled spectrograms) as well as several input variables and neural network learning techniques to determine the effect that they have on the overall accuracy of the genre classification network. This thesis demonstrates these convolutional neural network techniques for music genre classification and assesses their viability and accuracy.