<p>Prediction of material removal-induced variations in the in-process thin-walled workpiece dynamics is crucial to develop a vibration-free machining process. Therefore, monitoring the varying vibration modes to adjust the cutting parameters along the toolpath is imperative. Natural frequencies are the key dynamic parameters in machining for monitoring and controlling the machining-induced vibrations and preventing resonance and machining instability. This study aims to identify the natural frequencies of thin-walled parts during milling using a non-contact sensor. An output-only autoregressive (AR) model with cutting sound signals and an autoregressive with exogenous inputs (ARX) model using the measured cutting forces and cutting sound are developed to predict the in-process dominant mode frequencies of a flexible part as the material is removed. The natural frequencies obtained by the developed models are compared with the experimental modal analysis results. The comparisons showed that the developed AR and ARX models can respectively predict the dominant in-process workpiece mode frequencies with maximum errors of 8.6% and 7.8%. These results reveal the potential of the proposed methodology for monitoring the machining of thin-walled parts to improve part quality and process productivity.</p>