In an effort to increase machining efficiency and minimize costs, research into tool condition monitoring (TCM) systems has focused on developing methods to allow for unmanned machining. For drilling processes, such systems typically use indirect approaches to monitoring the tool condition by measuring spindle torque and feed force as well as vibrations including acoustic emission (AE – mechanical vibrations faster than 100 kHz). This project aimed to advance the state-of-the-art in the area of TCM by developing a method to detect sudden tool failures in large diameter (> 25 mm) indexable insert drills. This project was a continuation of the research conducted by Mr. R. Griffin (a former MSc student), who developed a model capable of predicting long term wear trends in indexable insert drills [1]. Notably, his model was unable to react to sudden tool breakage due to tool chipping, which was addressed by this project as presented in this thesis. In order to develop and train models able to detect sudden tool failure, an experiment was developed and installed in the field of the industry partner of this project. The experiment’s main feature was a pair of AE sensors added to the existing torque and force sensors. On this setup, experiments were conducted by drilling 2251 holes in workpieces using indexable insert drills with or without the insert breaking. When drilling holes without the insert breaking, the holes were named as good ones; and when drilling holes with the insert breaking they were named as bad holes. During the drilling process, data was collected from current sensors attached to the spindle motor and feed motor as well as from an AE sensor on the spindle and on the workpiece. From the signals from the spindle motor current and feed motor current sensors, algorithms were developed to identify and divide the signals of drilling a hole into different sections of the drilling cycle (i.e. entrance, steady-state, exit, etc.). Steady-state time-domain features were extracted from the sensor signals measured for all holes drilled in the experiments and the extracted features were used to train and test the classifier models. These models were cross validated to determine which type of model was the best fit for the drilling data collected. The results from the classifier models show that most of the classifiers tested have the ability to identify sudden tool breakage based on the data recorded in the present study, with varying degrees of success. The naïve Bayes classifier was able to detect the most failures but suffered from a large number of falsely detected failures. Both the classification tree and linear discriminant analysis classifiers had lower failure detection rates than the naïve Bayes classifier, but did not suffer from the same amount of false positives; as such, these two classifiers had higher overall classification rates than the naïve Bayes. These results suggest that classification tree and linear discriminant analysis methods are better suited for the drilling application and that the time-domain features should be complemented by others, such as the features extracted from the frequency domain, to accurately diagnose the tool condition. Future research should focus on extracting frequency and time-frequency domain features as these features might contain more information on tool condition. In addition, methods of examining features at the entrance and exit of the holes should be investigated as these two points in the drilling cycle are the most prone to sudden tool failure.