As the tool wears during a machining operation, the texture of the machined surface varies dramatically. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. It is more practical and suitable to analyze the machined surface than to measure the wear of the cutting tool directly. This paper discusses our work, which involves fractal analysis of texture of workpiece surfaces that have been subjected to end milling operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by computing the fractal features of texture images. The hidden Markov model is used to differentiate the various states of tool wear. Our result shows that fractal features can be effectively used to monitor the tool wear.