This paper presents a comprehensive review of sensor monitoring and advanced signal processing techniques for machining operations. It discusses the evolution of sensor monitoring in machining, including tool condition monitoring, unmanned machining, process control, and recent advancements in sensor fusion and signal processing. The paper reviews past contributions of CIRP in these areas and provides an up-to-date survey of sensor technologies, signal processing, and decision-making strategies for process monitoring. Application examples, including reconfigurable sensor systems, are reported. Future challenges and trends in sensor-based machining operation monitoring are also presented. The paper outlines the typical machining process monitoring system, which involves measuring process variables such as cutting forces, vibrations, acoustic emission, noise, temperature, and surface finish. These variables are influenced by the cutting tool state and material removal process conditions. Sensors are used to measure these variables, and the signals are processed to generate functional signal features correlated with tool state and process conditions. These features are then used in cognitive decision-making systems for final diagnosis, which can be communicated to the human operator or fed to the machine tool numerical controller for adaptive/corrective actions. The paper discusses various sensor technologies used in machining, including motor power and current, force and torque, acoustic emission, vibration, and other sensor types. It also covers advanced signal processing techniques such as time domain, frequency and time-frequency domain analysis, wavelet transform, and principal component analysis. These techniques are used to extract features from sensor signals for tool condition monitoring and process optimization. The paper highlights the importance of feature selection and the use of advanced signal processing methods to improve the accuracy and reliability of machining process monitoring. It also discusses the challenges and future trends in sensor-based machining operation monitoring.This paper presents a comprehensive review of sensor monitoring and advanced signal processing techniques for machining operations. It discusses the evolution of sensor monitoring in machining, including tool condition monitoring, unmanned machining, process control, and recent advancements in sensor fusion and signal processing. The paper reviews past contributions of CIRP in these areas and provides an up-to-date survey of sensor technologies, signal processing, and decision-making strategies for process monitoring. Application examples, including reconfigurable sensor systems, are reported. Future challenges and trends in sensor-based machining operation monitoring are also presented. The paper outlines the typical machining process monitoring system, which involves measuring process variables such as cutting forces, vibrations, acoustic emission, noise, temperature, and surface finish. These variables are influenced by the cutting tool state and material removal process conditions. Sensors are used to measure these variables, and the signals are processed to generate functional signal features correlated with tool state and process conditions. These features are then used in cognitive decision-making systems for final diagnosis, which can be communicated to the human operator or fed to the machine tool numerical controller for adaptive/corrective actions. The paper discusses various sensor technologies used in machining, including motor power and current, force and torque, acoustic emission, vibration, and other sensor types. It also covers advanced signal processing techniques such as time domain, frequency and time-frequency domain analysis, wavelet transform, and principal component analysis. These techniques are used to extract features from sensor signals for tool condition monitoring and process optimization. The paper highlights the importance of feature selection and the use of advanced signal processing methods to improve the accuracy and reliability of machining process monitoring. It also discusses the challenges and future trends in sensor-based machining operation monitoring.