Tool wear in machining is difficult to predict due to large number of influencing variables and tool-to-tool performance variation. In this presentation, machine learning classification methods, Support Vector Machines, and Logistic, for modeling tool life using production shop-floor tool wear data will be presented. Results show good agreement with the ‘true’ tool life curve. A simplified method to generate synthetic data to augment sparse and unbalanced datasets is also presented.