Tool wear is an important factor in determining machining productivity. In this paper, tool wear is characterized by remaining useful tool life in a turning operation and is predicted using spindle power and a random sample path method of Bayesian inference. Turning tests are performed at different speeds and feed rates using a carbide tool and MS309 steel work material. The spindle power and the tool flank wear are monitored during cutting; the root mean square of the time domain power is found to be sensitive to tool wear. Sample root mean square power growth curves are generated and the probability of each curve being the true growth curve is updated using Bayes' rule. The updated probabilities are used to determine the remaining useful tool life. Results show good agreement between the predicted tool life and the empirically-determined true remaining life. The proposed method takes into account the uncertainty in tool life and the growth of the root mean square power at the end of tool life and is, therefore, robust and reliable.