Tool status is crucial for maintaining workpiece quality during machine processing. Tool wear, an inevitable occurrence, can degrade the workpiece surface and even cause damage if it becomes severe. In extreme cases, it can also shorten the machine tool service life. Therefore, accurately assessing tool wear to avoid unnecessary production costs is essential. We present a wear classification model using machine vision to analyze tool images. The model categorizes wear images on the basis of predefined wear levels to assess tool life. The research involves capturing images of the tool from three angles using a digital microscope, followed by image preprocessing. Wear measurement is performed using three methods: gray-scale value, gray-level co-occurrence matrix, and area detection. The K-means clustering technique is then applied to group the wear data from these images, and the final wear classification is determined by analyzing the results of the three methods. Additionally, we compare the recognition accuracies of two models: support vector machine (SVM) and convolutional neural network (CNN). The experimental results indicate that, within the same tool image sample space, the CNN model achieves an accuracy of more than 93% in all three directions, whereas the accuracy of the SVM model, affected by the number of samples, has a maximum of only 89.8%.