Estimation of soil organic matter (SOM) at unsampled locations is crucial in agronomical and environmental studies. In this study, the ability of geostatistical methods such as ordinary kriging (OK), simple kriging (SK) and cokriging (CK) and hybrid geostatistical methods such as regression-simple kriging (RSK)/-ordinary kriging (ROK) and artificial neural network-simple kriging (ANNSK)/-ordinary kriging (ANNOK) was evaluated to predict SOM content. To this end, a set of 100 soil samples were collected from 0 to 15 cm depth of agricultural soils in Selin plain, northwest of Iran. The organic carbon was measured using Walkley-Black method. An auxiliary variable was provided by remote sensing data (Landsat 7 ETM+). Three performance criteria including mean error (ME), root mean square error (RMSE) and coefficient of determination (R2) were used to evaluate the performance of the derived models. The results showed that the ANN model that used principal components (PCs) as input variables, performed better than the multiple linear regression (MLR) model. The hybrid geostatistical methods, which include ANNOK, ANNSK, ROK and RSK provided more reliable predictions than the geostatistical methods, which include SK, OK and CK. In general, the best prediction method for the estimation of SOM spatial distribution was the ANNOK model, which had the smallest RMSE (0.271%) and the highest R2 (0.633). It was concluded that information from Landsat ETM+ imagery is potential auxiliary variables for improving spatial prediction, monitoring SOM and development of high quality SOM maps, which is the primary step in site-specific soil management.