Extraction and utilization of the horizon contour is presented within the context of an optical collision avoidance instrument, comprised of individual nodes configured in a fixed topology distributed network. The algorithm iterates between learning and application stages. Pixel neighbourhoods were classified into ground and sky regions using a vector of feature descriptors. The clusters in feature space were separated by a learnt minimal-error threshold, which was subsequently applied to the entire image, or a pre-selected region of-interest aided by scenario-dependent constraints. Morpho logical operations reduced spurious clusters. The resultant contour was parametrically fitted to a polynomial for comparison to ground-truth. Adaptive operation allows inputs from prior measurements and external attitude information.