The visual appearance of manufactured products is often one of the major quality attributes for certain types of products, which are used mainly for display purposes or used as the exterior part of other products. TFT-LCD (thin film transistor liquid crystal display) glass substrates can serve as a representative case. In such cases, visual quality (i.e., visual appearance), as well as the physical or mechanical quality attributes, has to be controlled or maintained. This paper presents an industrial case study of a machine vision methodology for the automatic grading of TFT-LCD glass substrates. In this case study, a classification model was developed using support vector machine (SVM), optimized via the simulated annealing (SA) algorithm. Parallel genetic algorithm (PGA) was also used to reduce the number of features for classification. The results show that utilization of an optimized SVM approach with SA in classification of TFT-LCD glass defects could be a viable alternative to manual classification in the TFT-LCD glass substrate industry.