This paper presents the methodology of surface roughness inspection in CNC Turning process. Adaptive Neural Fuzzy Inference System classifier utilizes to predict the high accuracy roughness value with insisting of surface roughness image. The vision system captures the image and determines the mean value by using ANFIS algorithm. Training sets variables speed, depth of cut, feed rate and mean value are feed as the input and manual stylus probe surface roughness value feed as the output. After the simulation process, the testing input performed and finally getting the vision measurement value. This higher accuracy (above 95%) and low error rate (below 4%) can be achieved by using the ANFIS classifier, which is predominantly helpful for the industry to measure the surface roughness. Assign the quality of the product by evaluating the manual stylus probe and vision measurement value.