Disclosed is an integrated imaging sensor/neural network controller for combustion control systems. The controller uses electronic imaging sensing of chemiluminescence from a combustion system, combined with neural network image processing, to sensitively identify and control a complex combustion system. The imaging system used is not adversely affected by the normal emissions variations caused by changes in burner load and flame position. By incorporating neural networks to learn emission patterns associated with combustor performance, control using image technology is fast enough to be used in a real time, closed loop control system. This advance in sensing and control strategy allows use of the spatial distribution of important parameters in the combustion system in identifying the overall operation condition of a given combustor and in formulating a control response accorded to a pre-determined control model.