Automated, closed loop method and system for monitor and control of semiconductor fabrication processing are described. Optical emissions spectrometer (OES) data readings from a fabrication plasma chamber are statistically analyzed and a novel pattern model (based on Markov random fields) is used in combination with a selective stochastic relaxation technique to identify gaseous species within the chamber from the OES readings. Wavelength and intensity information is also employed to accurately estimate relative concentration levels of identified gases within the chamber. The unique statistical analysis approach described allows real-time monitor and control of physical processing within the fabrication chamber. Several practical algorithms are set forth, including techniques for OES peak identification, peak sharpening gas identification, and physical processing control.