Chloronaphthalenes (PCNs, polychlorinated naphthalenes) are a group of persistent environmental pollutants. In the present study, geometrical optimization and electrostatic potential calculations have been performed for all 75 PCNs at the HF/6-31G* level of theory. A number of statistic based parameters have been extracted. Linear relationships between gas-chromatographic retention index (RI) of 62 PCNs in a non-polar column (DB-5) and the structural descriptors have been established by stepwise multiple regression technique. The result shows that two quantities derived from electrostatic potential on molecular surface, ∑ Vs- and σ+2, together with the number of chlorine ( N Cl) and the energy of the highest occupied molecular orbital (EHOMO) can be well used to express the quantitative structure-retention relationship (QSRR) of PCNs. Predictive capability of the model has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient ( Rc 2v) of 0.997, and further compared with the results from similar researches published recently. Furthermore, when splitting the 62 PCNs into training and validation sets in the ratio of 2:1, a similar treatment yields an equation of almost equal statistical quality and very similar regression coefficients, validating the robustness and prediction capability of our model. The QSRR model established may provide a new powerful method for predicting chromatographic properties of polychlorinated naphthalenes. Chloronaphthalenes (PCNs, polychlorinated naphthalenes) are a group of persistent environmental pollutants. In the present study, geometrical optimization and electrostatic potential calculations have been performed for all 75 PCNs at the HF / 6-31G * level of theory. A number of statistic based The linear relationships between gas-chromatographic retention index (RI) of 62 PCNs in a non-polar column (DB-5) and the structural descriptors have been established by stepwise multiple regression technique. The result shows that two quantities derived from electrostatic potential on molecular surface, Σ Vs- and σ + 2, together with the number of chlorine (N Cl) and the energy of the highest committed molecular orbital (EHOMO) can be well used to express the quantitative structure-retention relationship QSRR) of PCNs. Predictive capability of the model has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient (Rc 2v) o f 0.997, and further compared with the results from similar researches published recently. When, when splitting the 62 PCNs into training and validation sets in the ratio of 2: 1, a similar treatment yields an equation of an almost equal statistical quality and very similar regression coefficients, validating the robustness and prediction capability of our model. The QSRR model established may provide a new powerful method for predicting chromatographic properties of polychlorinated naphthalenes.