Uniformity is one of the issues of most critical concern for laser electrophotographic (EP) printers. Typically, full coverage constant-tint test pages are printed to assess uniformity. Exemplary nonuniformity defects include mottle, grain, pinholes, and “finger prints". It is a real challenge to make an overall Print Quality (PQ) assessment due to the large coverage of a letter-size, constant-tint printed test page and the variety of possible nonuniformity defects. In this paper, we propose a novel method that uses a block-based technique to analyze the page both visually and metrically. We use a grid of 150 pixels × 150 pixels ( ¼ inch × ¼ inch at 600-dpi resolution) square blocks throughout the scanned page. For each block, we examine two aspects: behavior of its pixels within the block (metrics of graininess) and behavior of the blocks within the printed page (metrics of nonuniformity). Both ΔE (CIE 1976) and the L* lightness channel are employed. For an input scanned page, we create eight visual outputs, each displaying a different aspect of nonuniformity. To apply machine learning, we train scanned pages of different 100% solid colors separately with the support vector machine (SVM) algorithm. We use two metrics as features for the SVM: average dispersion of page lightness and standard deviation in dispersion of page lightness. Our results show that we can predict, with 83% to 90% accuracy, the assignment by a print quality expert of one of two grades of uniformity in the print.