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Due to the high environmental risks and negative impact of a failure, tailings storage facilities (TSFs) need constant monitoring. Advanced mathematical models have been developed in the past to predict the behavior of TSFs and raise alerts if needed. To be precise and reliable, such models need a spatial distribution of soil types within the dam as an input. Getting this data from laboratory measurements is time and cost-consuming. In this article, we propose an ANN-powered algorithm, which allows us to accurately estimate the soil distribution based on a cone penetration test (CPT).