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Polymer composites reinforced with natural fibers and/or waste particulate fillers have been gaining an increasing popularity over the last few decades because of their environmental friendliness, biodegradability, and lower cost. Considering this, the present work is focused on the development, characterization, and dry-sliding wear response of hybrid composites consisting of polyester as the matrix and marble dust (industrial/construction waste) along with short kenaf fibers as the reinforcements. The physical and mechanical characterizations of the hybrid composites are conducted following ASTM standards in regard to their density, tensile, flexural, and impact strength. Dry sliding wear trials on the composite samples are conducted as per ASTM G99-05 under different test conditions using Taguchi’s L25 orthogonal array. Analysis of test results reveals that the filler content in the composite followed by sliding velocity has a significant effect on the wear rate. Analyzing the micrographs of the worn composite surfaces, it is observed that microcracking, plastic deformation of matrix, formation of wear debris, plowing on the composite surface are the major causes of wear loss of the composites. A prediction model based on artificial neural networks (ANN) is used to predict the SWR of the composites in a range wider than the experimental domain.