Choosing the right RPLC column for an actual separation among the more than 600 commercially available ones still represents a real challenge for the analyst particularly when basic solutes are involved. Many tests dedicated to the characterization and the classification of stationary phases have been proposed in the literature and some of them highlighted the need of a better understanding of retention properties to lead to a rational choice of columns. However, unlike classical chromatographic methods, the problem of their robustness evaluation has often been left unaddressed. In the present study, we present a robustness study that was applied to the chromatographic testing procedure we had developed and optimized previously. A design of experiment (DoE) approach was implemented. Four factors, previously identified as potentially influent, were selected and subjected to small controlled variations: solvent fraction, temperature, pH and buffer concentration. As our model comprised quadratic terms instead of a simple linear model, we chose a D-optimal design in order to minimize the experiment number. As a previous batch-to-batch study [K. Le Mapihan, Caractérisation et classification des phases stationnaires utilisées pour l'analyse CPL de produits pharmaceutiques, Ph.D. Thesis, Pierre and Marie Curie University, 2004] had shown a low variability on the selected stationary phase, it was then possible to split the design into two parts, according to the solvent nature, each using one column. Actually, our testing procedure involving assays both with methanol and with acetonitrile as organic modifier, such an approach enabled to avoid a possible bias due to the column ageing considering the number of experiments required (16 + 6 center points). Experimental results were computed thanks to a Partial Least Squares regression procedure, more adapted than the classical regression to handle factors and responses not completely independent. The results showed the behavior of the solutes in relation to their physico-chemical properties and the relevance of the second term degree of our model. Finally, the robust domain of the test has been fairly identified, so that any potential user precisely knows to which extend each experimental parameter must be controlled when our testing procedure is to be implemented.