This paper demonstrates how image processing techniques can be combined to automate print quality assessment and reduce the time to market. Typically, significant engineering resource is required to evaluate and optimize print quality over a range of environmental conditions. This method is not only tedious, but its subjective nature induces error. An automated process has been developed to quantify print quality for a variety of digitized print samples. The process uses neighborhood analysis and edge detection to automatically align the digitized image. Techniques such as gradient-based edge detection, neighborhood analysis, and thresholding are used to quantify print quality for each region of interest. Calibration curves are used to relate manually graded images to computationally graded images. Finally, a weighted normalized least-squares fitting routine transforms print quality metrics for each environmental condition into a table of optimized electrophotographic voltage settings.