Target classification algorithms have generally kept pace with developments in the academic and commercial sectors since the 1970s. However, most recently, investment into object classification by internet companies and various Human Brain Projects have far outpaced that of the defense sector. Implications are noteworthy. There are some unique characteristics of the military classification problem. Target classification is not solely an algorithm design problem, but is part of a larger system design task. The design flows down from a concept of operations (ConOps) and key performance parameters (KPPs). Inputs are image and/or signal data and time-synchronized metadata. The operation is real-time. The implementation minimizes size, weight and power (SWaP). The output must be conveyed to a time-strapped operator who understands the rules of engagement. It is assumed that the adversary is actively trying to defeat recognition. The target list is often mission dependent, not necessarily a closed set, and may change on a daily basis. It is highly desirable to obtain sufficiently comprehensive training and testing data sets, but costs of doing so are very high and data on certain target types are scarce. The training data may not be representative of battlefield conditions suggesting the avoidance of highly tuned designs. A number of traditional and emerging target classification strategies are reviewed in the context of the military target problem.