For the e$cient and reliable operation of automated machining processes, the implementation of suitable tool condition monitoring (TCM) strategy is required. Various monitoring systems, utilising sophisticated signal processing techniques, have been widely researched for a number of di!erent processes. Most monitoring systems developed up to date employ force, acoustic emission and vibration, or a combination of these and other techniques with a sensor integration strategy. With this work, the implementation of a monitoring system utilising simultaneous vibration and strain measurements on the tool tip, is investigated for the wear of synthetic diamond tools which are speci"cally used for the manufacturing of aluminium pistons. Contrary to many of the earlier investigations, this work was conducted in a manufacturing environment, with the associated constraints such as the impracticality of direct measurement of the wear. Data from the manufacturing process was recorded with two piezoelectric strain sensors and an accelerometer, each coupled to a DSPT Siglab analyser. A large number of features indicative of tool wear were automatically extracted from di!erent parts of the original signals. These included features from the time and frequency domains, time-series model coe$cients (as features) and features extracted from wavelet packet analysis. A correlation coe$cient approach was used to automatically select the best features indicative of the progressive wear of the diamond tools. The self-organising map (SOM) was employed to identify the tool state. The SOM is a type of neural network based on unsupervised learning. A near 100% correct classi"cation of the tool wear data was obtained by training the SOM with two independent data sets, and testing it with a third independent data set.