Many algorithms and their modifications have been used to design and optimize open pit limits. These algorithms have provided mine planning engineers with pertinent information in designing, optimizing and extracting ore reserves by open pit technology. However, they do not address the random field properties associated with the ore grades and reserves, and thus, fail to yield the truly optimized pit limits in any time horizon. In this paper, a new algorithm, MCS/MFNN, which overcomes these limitations is developed to optimize open pit limits. The random field properties of the ore grade and reserves have been modelled using the modified conditional simulation based on the best linear unbiased estimation and local average subdivision techniques. Artificial neural networks are then used to classify the blocks based on their conditioned economic block values. The error back propagation algorithm, in the neural networks, is used to optimize the pit limits by minimizing the desired and actual outputs error in a multilayer perceptron under the pit wall slope constraints. The MCS/MFNN algorithm is used to optimize a section of the gold deposit of the Sabi Heap Leaching Gold Project in Zimbabwe. The results of this algorithm are the same as that obtained from the Lerchs-Grossman's 2D algorithm, but in random multivariable states, the MCS/MFNN algorithm is the most suitable for pit optimization. The method is also fast since it mimics the operation of the brain in the solution of the pit optimization problem.