This study considers realistic data for a planned open pit iron ore mine, but is applicable to any open pit situation. By interpolating drill hole data, grades are generated for a rectangular block model. Each block's grade designation vector has components for each analyte (chemical element or compound) influencing ore value. Interpolation by means of geostatistical kriging leads to each block being assigned its expected value, and thus underestimates the overall grade variability. Alternatively, interpolation by means of conditional simulation is a method that implements random sampling from an infinite population of solutions. Each conditional simulation has appropriate overall grade variability, but estimating any block's mean and variance requires sampling from multiple conditional simulations. For a block model, an ore/waste selection criterion maximises the expected tonnage at a target grade. This criterion is a linear composite of the grade components, with positive coefficients for the beneficial analyte (iron) and negative coefficients for the deleterious analytes (such as silica, alumina and phosphorus).Although conditional simulation gives the same expected grade as kriging for each block, the expected maximum tonnage of ore selectable at a target grade may differ from that obtainable from the kriged solution. We apply the linear composite selection criterion to each of 25 conditional simulations, as well as to the kriged block model. The resulting distribution of product tonnage is confirmed to have an expected tonnage that is over 20 per cent greater than that of the kriged model. The method also enables a selection probability to be computed for each block, and thus a probabilistic pit boundary distribution to be identified and used in mine planning. Proposed extensions will consider risk-based scheduling of the multiple selection solutions through minimisation of a derived stress factor' and treating the mining process as an iterative system with actual or artificial depletions' modelled in line with the mine plan, using the updated state (with new information) to re-evaluate the mine plan for subsequent periods.CITATION:Everett, J E and Grobler, F, 2013. Risk-based ore selection from conditionally simulated block models, in Proceedings Iron Ore 2013 , pp 149-156 (The Australasian Institute of Mining and Metallurgy: Melbourne).