Mineral resources are a formal quantification of naturally occurring materials. Estimation of resource parameters such as grade and thickness may be carried out using different methodologies. In this paper, a soft methodology, which is artificial neural network (ANN) based fuzzy modelling is presented for grade estimation and its stages are demonstrated. The neuro-fuzzy method uses preliminary clustering and finally estimates the ore grades based on radial basis neural network and interpolation. Two case studies designed for both simulated and real data sets indicate that the approach is relatively accurate and flexible. In addition, the method is suitable for modelling via limited number of data. The results and performance comparisons with conventional methods show that the computing method is efficient. (C) 2009 Elsevier B.V. All rights reserved.