Appropriate on-line tool condition monitoring is essential for sophisticated and automated modern machine tools for aiding better tool management It enables higher productivity and safety to the machine-fixture-tool-work system. This paper presents a neural-networks- based system for on-line assessment of TiN-coated carbide inserts. The wear estimates by the system are observed to have very close agreement with the directly measured flank wear.