The present paper discusses response surface methodology (RSM) as an efficient approach for predictive model building and optimization of chromium adsorption on developed activated carbon. In this work the application of RSM is presented for optimizing the removal of Cr(VI) ions from aqua solutions using activated carbon as adsorbent. All experiments were performed according to statistical designs in order to develop the predictive regression models used for optimization. The optimization of adsorption of chromium on activated carbon was carried out to ensure a high adsorption efficiency at low adsorbent dose and high initial concentration of Cr(VI). While the goal of adsorption of chromium optimization was to improve adsorption conditions in batch process, i.e., to minimize the adsorbent dose and to increase the initial concentration of Cr(VI). In the adsorption experiments a laboratory developed Tamarind wood activated carbon made of chemical activation (zinc chloride) was used. A 2(4) full factorial central composite design experimental design was employed. Analysis of variance (ANOVA) showed a high coefficient of determination value (R-2 = 0.928) and satisfactory prediction second-order regression model was derived. Maximum chromium removal efficiency was predicted and experimentally validated. The optimum adsorbent dose, temperature, initial concentration of Cr(VI) and initial pH of the Cr(VI) solution were found to be 4.3 g/l, 32 degrees C, 20.15 mg/l and 5.41 respectively. Under optimal value of process parameters, high removal (>89%) was obtained for Cr(VI). (C) 2009 Published by Elsevier B.V.