We are developing a flexible and general methodology for real-time monitoring of acoustic emissions in machining applications. The goal of this work is to develop an approach to in-process monitoring which allows continuous assessment of tool wear and early warning of process exceptions. The nature of metal removal processes creates short-lived vibrations that carry information about the condition of the cutting tool and quality of cut. We wish to extract and represent these transient events without loss of important spectral structure. Other challenges include the need for system training data selection in the absence of expert labeled data, the modeling of short-term time evolution, and efficient real-time operation on an inexpensive computing platform. We present a system that meets these challenges through the use of high resolution time-frequency representations, vector quantization, hidden Markov models and a novel method of regrouping training data to refine initial class guesses. Applying this system to the classification of milling transients, we show that our system is capable of extracting these events and assigning them to meaningful classes - a crucial step in monitoring tool wear.