Geostatistical simulation has been widely used in mineral resource classification of metalliferous deposits based on international standards such as JORC code which is one of the most extensively used around the world for reporting the mineral resources and ore reserves alongside with objectives of the companies (Sadeghi, Madani and Carranza, 2015). However, the modeling process becomes challenging whenever the relationship between the variables of interest is complicated and cumbersome to model. This characteristic sometimes manifests itself in complex spatial and global cross-correlation measures, deeply depending on the formation and genesis of the deposit. The objective then is to generate the models that not only reproduces these significant characteristics, but also, it leads to a better understanding of a mineral resource estimation and successive decision-makings. In this respect, there are different approaches taking into account the spatial correlation structures, for which can be classified as independent simulation, co-simulation, and factor-based methodologies. In this paper, three techniques: Turning Bands Independent Simulation (TBSIM), Turning Bands Co-Simulation (TBCOSIM) and Projection Pursuit Multivariate Transform (PPMT); from those aforementioned approaches are selected to model two cross-correlated variables (Iron and Aluminum Oxide) belonging to an Iron ore deposit located in Brazil, in order to examine which methodology better reproduces the corresponding complexity in terms of local correlation measure. CITATION: Battalgazy, N and Madani, N, 2018. Mineral resource assessment in complex ore deposits based on international standards, in Proceedings Complex Orebodies Conference 2018, pp 79-81 (The Australasian Institute of Mining and Metallurgy: Melbourne).