Traditional estimation techniques based on block models with interpolation such as inverse distance and kriging methods do not take into account the uncertainty associated with the estimates and variability of a deposit. These methods are also inadequate for short range mine planning. However, conditional simulation models (Sequential Gaussian Simulation) reproduce the actual variability (histogram) and spatial continuity (variogram) of the attributes of interest. Sequential Gaussian Simulation (SGS) method was used to address the problem of measuring the uncertainty associated with an estimate for Cayirhan coal deposits. The coal seam is split by a 0.5-1 in thick tuffaceous siltstone and claystone into the upper (Tv), and lower (Tb) seams, which contain essentially different mineral matter and quality parameters. Directional and omni-directional experimental semivariograms of the variables for the transformed data showed that neither geometric nor zonal anisotropy exist in the data. The most evident spatial dependence structure of the continuity for omnidirectional experimental variogram, characterised by spherical and experimental models of the quality variables were obtained. The case study addresses the spatial distribution and uncertainty of quality variables at the study area using a probabilistic approach. This approach was based on SGS used to yield a series of conditionally images characterised by equally probable spatial distributions of the quality variables across the area. The advantages of SGS were illustrated through the case study. The simulated models can be incorporated into mine planning and scheduling.