The visual performance of liquid crystal displays (LCDs) has usually been evaluated by visual inspection during the manufacturing process. One of the visual problems hardest to recognize are regions of low contrast and nonuniform brightness called mura. The accurate and consistent detection of the mura is extremely difficult because there are various shapes and sizes of mura and the inspection results tend to depend on the operators. We conducted a study on the quantitative evaluation of mura based on visual analysis, intending to clarify the detection method and create an automated mura inspection process. We developed an algorithm and a hardware system based on a commercially available charge-coupled-device camera and a personal computer system with an image processor board. This system can successfully identify and evaluate mura. The algorithm was developed from research on visual analysis and human perception. We converted the front-of-screen images from the LCDs into distributions of luminance information, and the mura regions were distinguished from the background area using our novel algorithm. Our identification method can also distinguish between the muras caused by flaws in the LCD cells and the intentionally designed nonuniform luminance distribution of the backlight.