Instrument precision is characterized by its signal-to-noise ratio, employing a new noise model. In this paper, this measure of precision of each instrument is used to characterize instrumentation, and an integration is achieved by jointly optimizing the feedback control law and the instrument signal-to-noise ratios to meet control system performance requirements. Iterative algorithms are proposed to find locally optimal solutions. Assuming that the signal-to-noise ratio is directly related to the instrumentation cost, this integration provides a systematic procedure to design a low cost control system. More importantly, this procedure identifies the performance-limiting components of a control system, identifies where to spend money on a system, and generates component design requirements from closed loop system performance criteria.