A sequence optimization strategy combining an artificial neural network (ANN) and a chromatographic response function (CRF) for chromatographic separation it? reversed-phase high-performance liquid chromatography has been proposed. Experiments were appropriately designed to obtain unbiased data concerning the effects of varying the mobile phase composition, flow-rate, and temperature. The ANN was then used to simultaneously predict the resolution and analysis time, which are the two most important features of chromatographic separation. Subsequently, a CRF consisting of resolution and analysis time was used to predict the optimum operating conditions for different specialized purposes. The experimental chromatograms were consistent with those predicted for given conditions, which verified the applicability of the method. Furthermore, the proposed optimization strategy was applied to literature data and very good agreement was obtained. The results show that a strategy of sequential combination of ANN and CRF can provide a more flexible and efficient optimization method for chromatographic separation. (C) 2009 American Institute of Chemical Engineers AIChE J, 56: 371-380, 2010