Choosing a suitable set of signals for measurement in classification procedures is poorly understood. Since classification is generally associated with a decision process, this paper shows how the entire question is most sensibly formulated as a two-stage statistical decision process. The first decision involves the detection of some suitable inferential signal, while the second is the selection of a suitable course of action or a control policy. The paper discusses techniques for choosing a set of signals for measurement, subject to certain constraints that are imposed upon this selection process, and demonstrates that the classification procedure must be considered in conjunction with the associated decision or control process, if the most appropriate inferential signals are to be selected. In this formulation, pure classification occurs if the possible actions are chosen in a particular manner. The selection of the best signal or signals for measurement may be influenced significantly by the time-dependent nature of the statistical risk.