<p>All machining processes involve vibrations originating from either the structural dynamics of the machinery system or the impact phenomena related to the interaction between the cutting tools and the workpieces. The latter can lead to a chip regeneration effect, which negatively impacts the machining quality, resulting in poor surface finishes or, in severe cases, damage to the machine tool or workpiece. Traditionally, analytical methods such as stability lobe diagrams and analytical models in the time and frequency domains have been employed to predict and mitigate chatter. These methods offer advantages such as simplicity, low computational cost, and ease of implementation. However, they often rely on assumptions that limit their accuracy, particularly under complex machining conditions. This paper presents the development of a novel analytical algorithm for chatter detection in the time domain, based on signal filtering and energy ratio evaluation. A comparison with the corresponding frequency domain and with other analytical approaches was conducted. The results show that the proposed method demonstrates performance for detection of chatter-free and severe chatter-affected machining processes. Moreover, the proposed analytical time domain algorithm (ATDA) exhibits greater sensitivity in handling non-stationary signals, especially in intermediate machining states, where moderate chatter is observed or where a severe transition from stable to unstable machining occurs. Furthermore, the evaluation of the computational costs confirmed that the proposed time domain method offers good computational performance, highlighting its suitability for in-process applications.</p>