In spite of recent advances in flow cytometry technology, most cytometry data is still analyzed manually which is labor-intensive for large datasets and prone to bias and inconsistency. We designed an automatic processing tool (APT) to rapidly and consistently define and describe cell populations across large datasets. Image processing, smoothing, and clustering algorithms were used to generate an expert system that automatically reproduces the functionality of commercial manual cytometry processing tools. The algorithms were developed using a dataset collected from CMV-infected infants and combined within a graphical user interface, to create the APT. The APT was used to identify regulatory T-cells in HIV-infected adults, based on expression of FOXP3. Results from the APT were compared directly with the manual analyses of five immunologists and showed close agreement, with a concordance correlation coefficient of 0.96 (95% CI 0.91-0.98). The APT was well accepted by users and able to process around 100 data files per hour. By applying consistent criteria to all data generated by a study, the APT can provide a level of objectivity that is difficult to match using conventional manual analysis.