As stated in the Office of the Secretary of Defense's Unmanned Aircraft Systems Roadmap 2005–2030, reconnaissance is the number one priority mission for Unmanned Air Vehicles (UAVs) of all sizes. During reconnaissance missions, classification of objects of interest (e.g, as friend or foe) is key to mission performance. Classification is based on information collection, and it has generally been assumed that the more information collected, the better the classification decision. Although this is a correct general trend, a recent study has shown it does not hold in all cases. This paper focuses on presenting methods to plan paths for unmanned vehicles that optimize classification decisions (as opposed to the amount of information collected). We consider an unmanned vehicle (agent) classifying an object of interest in a given area. The agent plans its path to collect the information most relevant to optimizing its classification performance, based on the maximum likelihood ratio. In addition, a classification performance measure for multiple measurements is analytically derived.