In this paper, the image processing for flexible disk grinding and the effect of the grinding conditions on the flat zone length of a workpiece are investigated, with the purpose of automating the grinding process. To accomplish this, three issues should be carefully studied. The first is finding the relationship between the flat zone length and the grinding conditions such as the cutting speed and feeding speed. The second is developing a neural network algorithm to predict the flat zone. The third is developing an image processing algorithm to measure the flat zone length of a workpiece. Slope analysis is used to determine straight and curved sections during the image processing. For verification, the estimated length and the length from the image processing are compared with the length measured by a projector. There is a minimum difference of 1.7% between the predicted and measured values. The results of this paper will be useful in compiling a database for process automation.