A statistical methodology is presented for optimally locating the sensors in a structure for the purpose of extracting from the measured data the most information about the parameters of the model used to represent structural behavior. The methodology can be used in model updating and in damage detection and localization. It properly handles the unavoidable uncertainties in the measured data as well as the model uncertainties. The optimality criterion for the senor locations is based on information entropy which is a unique measure of the uncertainty in the model parameters. The uncertainty in these parameters is computed by the Bayesian statistical methodology and then the entropy measure is minimized over the set of possible sensor configurations using a genetic algorithm. The information entropy measure is also extended to handel large uncertainties expected in the pre- test nominal model of a structure. In experimental design, the proposed entropy-based methodology provides a rational procedure for comparing and evaluating the benefits of adding more sensors in the structure against the benefits of exciting and observing (measuring) more modes using the existing number of sensors. Simplified models for building and bridge structures are used to illustrate the methodology.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.