In this study, two kriging methods using an auxiliary variable, i. e., ordinary cokriging (OCK) and ordinary kriging combined with regression (OKR) were used for the interpolation of soil zinc (0.1 mol x L(-1) HCl extractable Zn) in a 17.6 hm2 field at the vicinity of a metal manufacturer in southern suburb of Shenyang, China. A total of 36 measured data of soil Zn content at the depth of 10 approximately 20 cm (subsoil Zn) was selected as target variable, 72 measured data at the depth of 0 approximately 10 cm (topsoil Zn) as auxiliary variable, while other 36 measured data of subsoil for validation. The two interpolation methods were evaluated for the suitability of estimating the spatial distribution of soil Zn by using an auxiliary variable. The results showed that OKR gave better results than OCK or ordinary kriging (OK). The theoretical model obtained from OKR exhibited higher coefficient of determination and lower residual sums of squares than that from OCK or OK. The prediction accuracy of soil Zn was increased by 4% with OKR than with OK. The map of soil Zn obtained with OKR was quite similar with that obtained with OK, by using 72 measured Zn data. However, no advantages were found between OCK and OK. It was suggested that OKR was an effective way to estimate the distribution of soil heavy metals by using auxiliary variables.