The work seeks to create a dataset of secondary election images of Ti-6Al-4V fatigue fracture surfaces and apply transfer learned Convolution Neural Networks (CNNs) to the dataset to make connections between the fracture surfaces and the fatigue life, with the goal being fractography by computer. The images consist of the crack initiation site, short crack region, steady crack growth, and instantaneous failure region on multiple samples with varying loading conditions and fatigue lifetimes. Unsupervised learning which consists of dimensionality reduction and clustering is used to determine which subset of data, i.e., which crack growth region or magnification, provides the most physically meaningful information. Additionally, the features of those images that are deemed most important provide information that can tie the fracture surface to the fatigue life and microstructure. The images taken, the algorithms used, identified fatigue properties, and fracture characteristics will be presented.