A methodology for generating optimal sensor network design for multirate systems is presented. Location of sensors, cost of measurement and frequency of sampling are important factors that have been incorporated in the sensor network design formulation. The proposed methodology is based on evaluating trade-off (Pareto optimal) solutions between the quality of state estimation and the total measurement cost associated with the sensor network. To accommodate different sampling frequencies and evaluate their effect on state estimation accuracy, a generic multirate extension of the traditional Kalman filter is used. In general, higher accuracies of the state estimates are realizable at expense of higher measurement cost. Incorporation of these conflicting objectives of minimizing measurement cost and maximizing estimation accuracy results in a combinatorial, implicit multiobjective optimization problem, which is solved using the well-known non-dominated sorting genetic algorithm-II. The resulting solutions can be then analyzed by the process designer for determining an appropriate sensor network. The methodology is demonstrated by generating optimal sensor network design for the benchmark quadruple tank set up [K.H. Johansson, The quadruple-tank process: a multivariable laboratory process with an adjustable zero, IEEE Trans. Control Syst. Tech. 8 (3) (2000) 456-465] and the Tennessee Eastman challenge process [J.J. Downs, E.F. Vogel, A plant-wide industrial process control problem, Comput. Chem. Eng. 17 (3) (1993) 245-255]. (C) 2007 Elsevier Ltd. All rights reserved.