This paper describes the development of multiphase hybrid composites consisting of polyester reinforced with E-glass fiber and ceramic particulates. It further investigates the erosion wear response of these composites and presents a comparison of the influence of three different particulate fillers—fly ash, alumina , and silicon carbide (SiC)—on the wear characteristics of glass-polyester composites. For this purpose, the erosion test schedule in an air jet type test rig is made, following design of experiments approach using Taguchi’s orthogonal arrays. The Taguchi approach enables us to determine optimal parameter settings that lead to minimization of the erosion rate. The results indicate that erodent size, filler content, impingement angle, and impact velocity influence the wear rate significantly. The experimental results are in good agreement with the values from the theoretical model. An artificial neural network approach is also applied to predict the wear rate of the composites and compared with the theoretical results. This study reveals that addition of hard particulate fillers such as fly ash, , and SiC improves the erosion resistance of glass-polyester composites significantly. An industrial waste such as fly ash exhibits better filler characteristics compared with those of alumina and SiC. Finally, a popular evolutionary approach known as genetic algorithm is used to generalize the method of finding out optimal factor settings for minimum wear rate.