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Among manufacturing companies there is a wide-spread consensus that women are better suited to perform visual quality inspection, having higher endurance and making decisions with better reproducibility. Up to now gender-differences in visual inspection decision making have not been thoroughly investigated. We propose a machine learning approach to model male and female decisions with classifiers and base the analysis of gender-differences on the identified model parameters. A study with 50 male and 50 female subjects on a visual inspection task of stylized die-cast parts revealed significant gender-differences in the miss rate (p = 0.002), while differences in overall accuracy are not significant (p = 0.34). On a more detailed level, the application of classifier models shows gender differences are most prominent in the judgment of scratch lengths (p = 0.005). Our results suggest, that gender-differences in visual inspection are significant and that classifier-based modeling is a promising approach for analysis of these tasks.