In response to the demand for soil spatial information, the acquisition of digital auxiliary data and their matching with field soil observations is on the increase. With the harmonization of these data sets, through computer based methods, the so-called Digital soil Maps are increasingly being found to be as reliable as the traditional soil mapping practices, and with no prohibitive costs. Therefore, in the present research, it has been attempted to Develop Decision Tree (DTA) and Artificial Neural Network (ANN) models for spatial prediction of soil taxonomic classes in an area covering about 720 km² located in an arid region of central Iran where traditional soil survey methods are very difficult to undertake. Within this using the conditioned Latin hypercube sampling method, location of 187 soil profiles were spotted and then described, sampled, analyzed and allocated in taxonomic classes according to soil taxonomy of America. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes, Landsat 7 ETM⁺ data and a geomorphologic surfaces map. Results revealed that DTA benefited from a the higher accuracy than ANN for about 7% as regarded the prediction of soil classes. A determination of coefficient (R²), overall accuracy and, Kappa coefficient calculated for the two models were recorded as 0.34, 0.46, 48%, 52%, and 0.13 vs. 0.25, respectively. The results revealed some auxiliary variables as having more influence on the predictive soil class model. Wetness index, geomorphology map and multi-resolution index of valley bottom flatness could be named as some of these variables. In general, results showed that decision tree models benefited from a higher accuracy than ANN ones, with results as more convenient for interpretation. Therefore, use of decision tree models for spatial prediction of soil properties (category and continuous soil data) is recommended in the future studies.