In milling operations, chatter vibration is one of the main factors that lower the productivity. This phenomenon is responsible of poor surface quality and increases cutting forces. Higher efforts tend to accelerate tool wear and can lead to tool breakage. Two main fields of research try to improve control of the process: prediction and on line detection. Prediction techniques simulate behavior of the machining system and try to anticipate vibratory behavior in order to compute optimal parameters for a given operation (spindle speed, depth of cut,...). On line detection techniques try to detect instability using sensors (force sensor, accelerometer, microphone,...) and signal processing. On line detection often needs fewer data about the studied system (sometimes restricted to current spindle speed) and can give important information for control techniques. In this paper, three chatter detection techniques are presented and experimentally tested. The first one is based on the measured signal level, the second ne studies the frequency content of the signal and the last one uses the variance of the signal sampled at a once per revolution rate. Each method is initially applied to a signal given by numerical simulation of the milling process. Ideal signal and signal disturbed by a random white noise are studied. This first step validates the three detection methods. These techniques are then used to analyze signals from milling operations tests. We used a microphone as chatter detection sensor, which can easily measure a signal without disturbing the system. Machined workpiece is clamped in a very simple structure designed to be as close as possible from a single degree of freedom system. This simplifies the frequency content of the signal and thus makes detection easier.