Laser cutting is one of the most important applications of laser in manufacturing industry; it is mainly used for sheet metal cutting. In laser cutting, performing real-time evaluation of laser cut quality is very important to the advancement of this process in industry. However, due to the dynamic nature of the laser cutting process specially when cutting ferrous alloys using oxygen as an assist gas, laser cut quality cannot be easily predicted; therefore, the quality inspection of the laser cut is performed by off line inspections of the edges of the metal by skilled operators. This methodology is carried out after the process and thus cannot maintain a good quality if the process performance is out of control. Therefore, the objective of the research project is to qualify and develop a sensor system that ensure fault recognition online and can automatically control the laser metal cutting process to achieve good quality cut. For the realization of this objective the following has been done: - study the relationship between process parameters and cut quality characteristics; - identify the best sensors that can be used to monitor the process; - design and develop an experimental setup to test the proposed sensors; - collect and analyze data from the proposed sensors and correlate them to specific cut quality characteristics (process state variables); - develop direct relationships between the process signals and cut quality; - develop appropriate strategy for process control; - design and develop an integrated monitoring and control system; - test and evaluate the proposed system using simulation. In this study, a new technique for the determination of cut quality of sheet steels under the CO2 laser cutting process has been established. It is based on on-line detection and post-processing analysis of light radiation and acoustic emissions from the cut kerf. Determination of machining quality during cutting is best done through the measurement of surface roughness and kerf widths, as these are the two parameters that vary in successful through cuts. These two quality parameters can further be correlated to the two dominant process parameters of laser power and cutting speed. This study presents an analysis of acoustic emissions and reflected light for CO2 laser cutting of steel plates, and discusses their use for the estimation of cut quality parameters of kerf width and striation frequency for mild steel plates of 3mm, 5mm, 8mm, and 10mm thicknesses. Airborne acoustic and light signals are acquired with a microphone and a photodiode respectively, and recorded with a PC based data acquisition system in real time. The signals are then analyzed to establish a correlation between the signals obtained and the cut quality achieved. Experimental evidence shows that the energy levels of acoustic emission signals (RMS analysis) can be used to maintain the cutting process under steady state condition. On the other hand, the light intensity signal fluctuates with a frequency that corresponds to the frequency of striations formed on the cut surface; therefore it can be used to regulate cutting speed and laser power to obtain an optimum cutting condition and best cut quality. The validity of the proposed control strategy was tested experimentally by simulating the variations of cutting speed and examining their effect on the signals. So far, the prototype used for experimentation has been successful in providing correct information about cut quality in terms of striation frequency, and also about the state of the process where the microphone signal was successful in determining system failure or improper cutting conditions. A microprocessor based control system utilizing the PID control algorithm is recommended for the implementation of the control strategy. The implementation requirements of the proposed system for industrial use are then discussed. A new setup for the coaxial monitoring of CO2 laser cutting using a photodiode is proposed to enhance the quality of the signal and also to protect the photodiode from the harsh cutting environment. It is also proposed that an open control architecture platform is needed to enhance the integration of the proposed process control functions. Conclusions and future research directions towards the achievement of Autonomous Production Cell (APC) for the laser cutting process are then given.