Currently, multiparameter flow cytometry immunophenotyping is the selected method for the differential diagnostic screening between reactive lymphocytosis and neoplastic B-cell chronic lymphoproliferative disorders (B-CLPD). Despite this, current multiparameter flow cytometry data analysis approaches still remain subjective due to the need of experienced personnel for both data analysis and interpretation of the results. In this study, we describe and validate a new automated method based on vector quantization algorithms to analyze multiparameter flow cytometry immunophenotyping data in a series of 307 peripheral blood (PB) samples. Our results show that the automated method of analysis proposed compares well with currently used manual approach and significantly improves semiautomated approaches and, that by using it, a highly efficient discrimination with 100% specificity and 100% sensitivity can be made between normal/reactive PB samples and cases with B-CLPD based on the total B-cell number and/or the sIgkappa+/sIglambda+ B-cell ratio. In addition, the method proved to be able to detect the presence of pathologic neoplastic B-cells even when these are present at low frequencies (<5% of all lymphocytes in the sample) and in poor-quality samples enriched in 'noise' events.