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Cutting tool wear is one of the major physical phenomena to be studied in order to optimize the production and to guarantee the quality of manufactured products. Indeed, the wear affects the quality of the machined surfaces, the durability of the cutting tool and the imposed geometric tolerances. Since uncontrolled wear can lead to premature tool breaking and therefore a drop in productivity, monitoring the machining process is a necessary important task. To evaluate the wear of a cutting insert while turning process, this paper aims to combine experimental results from vibration and cutting forces with numerical methodologies based on the application of several signal-processing techniques, especially the Optimized Wavelet Multi-Resolution Analysis (OWMRA) to analyze the measured cutting forces signals during the cutting process. The main objective is to find a correlation between the wear and several indicators established from these measured signals, allowing the prediction of the tool wear as early as possible. The results obtained after the application of the OWMRA allowed the denoising of the measured signals and revealed two peaks, which appear below and above the tool’s resonance frequency. The amplitude evolution of these two peaks is directly related to the effect of the tool wear on the natural frequency band.