Constant illumination is often critical to inspection quality in machine vision systems. In industry however, it can be difficult to eliminate noise due to uneven and/or changeable illumination. This research focuses on design of an intelligent automatic thresholding component to make vision systems insensitive to noise and to increase inspection accuracy. Analysis of Variance was used to identify factors contributing most to inspection quality. Regression analysis was utilized to formulate a mathematical model based on the relationship of the thresholding value to light intensity. A component was then designed to receive environmental illumination readings from a sensor, predict appropriate thresholding values using the mathematical model, and feed this information to a vision system to adjust inspection settings, thereby allowing the vision system to be robust with respect to illumination noise. In addition, a framework for developing a learning algorithm which integrates inspection results, corresponding illumination noise, and the proposed mathematical model is presented. Simulation indicates that adding this learning algorithm to the proposed component design should result in a lower error rate as the mathematical model is gradually tuned with information about inspection results.