Spectral imaging is an emerging tool for defense and security applications because it provides compositional information about the objects in a scene. The underlying task-measuring a 3-D dataset using a 2-D detector array-is challenging, and straightforward approaches to the problem can result in severe performance tradeoffs. While a number of ingenious (non-adaptive) solutions have been proposed that minimize these tradeoffs, the complexity of the sensing task suggests that adaptive approaches to spectral imaging are worth considering. As a first step towards this goal, we investigate adaptive spectroscopy and present initial results confirming dramatic cost/performance gains for a particular implementation.