The maturity reached in the development of Unmanned Air Vehicles (UAVs) systems is making them more and more attractive for a vast number of civil missions. Clearly, the introduction of UAVs in the civil airspace requiring practical and effective regulation is one of the most critical issues being currently discussed. As several civil air authorities report in their regulations “Sense and Avoid” or “Detect and Avoid” capabilities are critical to the successful integration of UAV into the civil airspace. One possible approach to achieve this capability, specifically for operations beyond the Line-of-Sight, would be to equip air vehicles with a vision-based system using cameras to monitor the surrounding air space and to classify other air vehicles flying in close proximity. This paper presents an image-based application for the supervised classification of air vehicles. First, several vehicle images, taken from different points of view, are transformed using a descriptor of salient features as to build the five-class database used to train the classification algorithm. Then, the latter compares the descriptor of a vehicle image taken from a random point of view to records in the database. With a positive match, the vehicle will be assigned to one of the following classes: a) civil transport aircrafts, b) military aircrafts, c) general aviation aircrafts, d) helicopters, and e) airships/hot air balloons. The paper provides a possible layout for the algorithm implementation and presents the outcome of several tests performed to evaluate its efficiency and possible exploitation. Indications useful to further studies are presented to help future researches.