On-line detection and measurements of tool wear is important to assure manufacturing accuracy, enhance manufacturing efficiency, and reduce manufacturing costs. In this research, adaptive neuro-fuzzy inference systems are utilized in conjunction with features extracted from three-axis cutting force data for the on-line detection and measurements of tool wear for precision boring of titanium components. Cutting force data were measured for carbide tools during the boring of titanium parts. At the end of every boring process, the average flank wear width was measured to determine the cutting tool conditions. Measurements were accomplished with the aid of a toolmaker’s microscope. In total, 14 features were obtained from the cutting force data. Euclidean distance measure was utilized to determine which features showed the best indication of cutting tool conditions. This approach can reduce the number of features for on-line detection and measurements of tool wear for precision boring of titanium parts. The selected two most prominent features were kurtosis of longitudinal force and average of the ratio between tangential force and radial force. On-line detection of boring tool wear obtained excellent results, using a 2x2 adaptive neuro-fuzzy inference systems, of being able to predict tool conditions on-line with 100% reliability. On-line measurements of boring tool wear also produced exceedingly successful results with a minimum error for a 1x10 adaptive neuro-fuzzy inference systems of 0.87%.