Least median squares (LMS) curve fitting is a method of robust statistics that guards the process of data analysis from perturbations due to the presence of outliers 1 . This procedure has several advantages over classic least squares (LS) curve fitting, especially in the noisy problem environments addressed by today's process-control engineers. Although LMS curve fitting is a powerful technique, there are some limitations to the LMS approach. However, these limitations can be overcome by combining the search capabilities of a genetic algorithm with the curve-fitting capabilities of the LMS method. Genetic algorithms are search techniques that model the search that occurs in nature via genetics. This paper presents a procedure for utilizing genetic algorithms in an LMS approach to curve fitting. Several examples are provided from a number of application areas, thereby demonstrating the versatility of the genetic-algorithm-based LMS approach.