Abstract: When a mariner navigates into an unfamiliar area, he/she uses a nautical chart to familiarize him/herself with the environment, determine the locations of hazards and decide upon a safe course of travel. An Autonomous Surface Vehicle (ASV) would gain a significant advantage if, like its human counterpart, it can learn to read and use the information from a nautical chart. Electronic Nautical Charts (ENCs) contain extensive information on an area, providing indications of rocks and other obstructions, navigational aids, water depths and shorelines. The goal of this research is to increase an ASV’s autonomy by using ENCs to guide the helm when its predetermined path which may be dynamically changing is unsafe due to known hazards to navigation and context to its sensor measurements that are invariably subject to uncertainty. Identifying objects in a camera, sonar, LIDAR or other sensor’s data can be a challenging endeavor in an ocean environment due to the variable sea state, the wind, fog, sea spray, sun glint from the sea surface and bubbles in the water column. Therefore, providing a prior probability distribution for the likely location of those objects in a sensor’s field of view has the potential to enhance object detection processing significantly. Contextualizing sensor measurements dynamic identifies objects from the ENC in a sensor’s field of view and provides that information to the sensor in real-time. It accomplishes these tasks, feature layers within a standard ENC must be translated to a spatial database. In this database, features are encoded with a “threat level” based on the feature type and the estimated depth of the object which is not always encoded within the ENC. Variations in the political tides as well as the vessel size and speed are also factors when deciding the threat level and the vehicle’s appropriate course of action.