A conventional, deterministic orebody model would lead to over estimation or under-estimation of the grade, volume and other parameters related to a deposit. This will lead to improper mine planning and thus incur huge financial risk. A proper orebody and grade modeling provide better confidence to mine owners regarding financial decision. However, only using few number of borehole data it is always difficult to come up with such type of accurate decision. Always there are certain amount of risk are associated with the estimation as well as decision. This thesis aims at providing a better risk assessment at minimizing the grade and volumetric uncertainty of the ore body. The multipoint simulation algorithms eliminate the demerits of variogram based geostatistics modeling and preserve multi-point information borrowed from training image. In this thesis, a case study of iron ore deposit from India is performed to analyses the volumetric and grade uncertainty the volumetric and grade uncertainty. Single normal equation simulation (SNESIM), a multi-point categorical simulation algorithm, was performed to measure the volumetric uncertainty of orebody. Ore volume uncertainty was performed by generating. 10 equiprobable orebody simulated models are developed. The grade uncertainty modeling was performed by applying sequential Gaussian simulation (SGM) with orebody model generated by SNESIM algorithm. The result shows that if the training image –based multi-point simulation is applied for ore body modeling, there would have been 7 % increase in volume as compared to traditional method. The grade-tonnage uncertainty reveals that uncertainty-based generates more high grade ores when compared with ordinary kriging method.