Counting of mitotic cells has been shown to be of prognostic value in breast cancer in different retrospective studies. Up to now the number of mitoses is assessed mainly manually according to a standardized but strict protocol. Although such a manual procedure is reasonably reproducible, automatic counting of mitotic cells offers the potential for greater objectivity and reproducibility. This paper describes the influence of resolution on automatic recognition by image processing of mitotic cells in Feulgen stained breast cancer sections. Using the image recording, correction and segmentation procedure described in a previous study, five specimens were analyzed: one was used to serve as a training set and four were put aside for later use as independent test set. For each slide, objects from a pre-selected area were recorded at increasing resolution. For each object, contour features and optical density measurements were computed and stored in a data file for statistical analysis. The results showed that increased resolution using a 40x objective lowered the number of misclassified mitoses compared with a 20x objective (overall mean percentage of misclassified mitoses over training and all test specimens: 20x, 24.57; 40x, 7.96). The number of misclassifications of non-mitoses was almost stable per specimen but varied between specimens (19-42%) due to differences among tissues. Given the improvement in classifying mitoses and the possibility to evaluate interactively the measurement result, the described semi-automated mitoses pre-screener of histological sections may be suitable for further testing in a clinical setting.