Use of sensors to monitor dynamic performance of machine tools at Ford's powertrain machining plants has proven to be effective. The traditional approach to convert sensor data to actionable intelligence consists of identifying single features from cycle based signatures and setting thresholds above acceptable performance limits based on trials. The thresholds are used to discriminate between acceptable and unacceptable performance during each cycle and raise alarms if necessary. This approach requires a significant amount of resource & time intensive set up work up-front and considerable trial and error adjustments. The current state does not leverage patterns that might be discernible using multiple features simultaneously. This paper describes enhanced methods for processing the data using supervised and unsupervised machine learning methods. The objective of using these methods is to improve the prediction accuracy and reduce up-front set up. Classifiers such as KNN, Logistic Regression with Lasso, SVM for supervised learning and Novelty Detection, Elliptic Envelope for unsupervised learning have been compared using confusion matrices and ROC curves. The paper also highlights the challenges with applying supervised techniques due to lack of tagged data for training classifiers in a manufacturing environment.