Condition of the tool is most significant factor in the manufacture of a quality product. Manufacturing a product involves a different types of cutting tools and the tool will wear out during product manufacturing. Using a worn-out tool would result in the formation of the defective product. Thus monitoring of cutting tools assumed special significance. Many researchers have been contributing techniques for tool wear measurement. An attempt has been made in this paper (i) for exploring the literature for the different tool wear monitoring techniques (ii) advantages and limitations of the different techniques (iii) research gaps and to provide directions for future research. Finally, the paper concludes that much research is required in the design and development of methods using the application of machine learning and artificial intelligence for achieving accuracy, robustness, and economy in tool condition monitoring. This would help in improving the reliability of the machine tool and in turn help deliver Quality products to the customer.