Under certain circumstances, there is a need to extract DTM data from topographic contour maps. The most common method is to interpolate the contours on the map into grid DTM data. Previous researches (reference) have demonstrated that the data quality of the DTM raster image is affected by the interpolation method employed, the quality of the original contour map, and the characteristics of the terrain. In terms of the interpolation method, existing GIS software provide traditional mathematical or statistical functions like IDW and kriging. In recent years, researchers (reference) have proposed artificial neural networks as an alternative approach to implement the spatial interpolation process. Artificial neural networks have been used in a broad range of applications, including pattern classification, pattern completion, function approximation, optimization, prediction, and automatic control. For spatial mapping roles, a function approximation or optimization technique may be applied, and researches have been investigating the use of these techniques with various kinds of neural network models, each focussing on a different aspect. Artificial neural networks are data-driven and do not require a priori knowledge of the study area. This paper investigates the use of neural networks for the generation of interpolated terrain data, and validates the results of the back error propagation model with reference to the real world.