In the last few decades, machine learning and computer vision techniques have enabled precise and repeatable image recognition. Computer vision techniques can also recognize star patterns in star trackers for satellite attitude determination. Horizon detection in the visible spectrum was largely discarded for attitude determination in favor of thermal imagery, due to the greater consistency of the earths thermal radiation. This thesis examines computer vision and machine learning techniques to develop a horizon detection algorithm for the visible spectrum. By examining different features of visual imagery, machine learning techniques were evaluated on the ability to detect a visible horizon and determine its orientation. An empirical analysis of visual imagery from low-earth orbit was conducted to develop a horizon brightness transition model, which allows for consistent and adjustable determination of the horizons location. The final result is a horizon detection and orientation determination algorithm that successfully indicates if a horizon is present in an image with 96 precision and 92 recall. The brightness model correctly identifies the location of the horizon in 85 of the tested image set.