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Natural or artificial light allows us to see and analyze matter with our eyes, which are the first tools used in several experiments. In geosciences, particularly in mineralogy, light is used for optical microscopy observations. Reflected and transmitted light applied to the study of ore deposits can be useful to discriminate between gangue from precious phases. Knowledge of the structural and morphological characteristics, combined with the quantitative evaluation of mineral abundance, is fundamental for determining the grade of ore deposits. The accuracy and reliability of the information are closely linked to the ability of the mineralogist, who more and more often uses Scanning Electron technology and automated mineralogy systems to validate the observations or solve complex mineralogy. While highly accurate, these methods are often prohibitively expensive. The use of image analysis using standard algorithms and artificial intelligence, available as open source, and commercial packages (such as ImageJ, Fiji or MATLAB), can provide advantages in fast, cost-effective, and robust mineral analysis. Recently, the application of neural networks provided increasingly effective image analysis and, among the different types of neural networks available today, the self-organizing maps of Kohonen (SOM) seem to be among the most promising, given their capacity to receive many images as inputs and reduce them to a low number of neuronal outputs that represent all the input characteristics in a lower-dimensional space. In this work, we will show the preliminary results of a new method based on SOM and the combined use of images acquired in transmitted and reflected light to reconstruct false 3D surfaces, which were able to show the presence of intergrow between gangue phases and precious minerals. [ABSTRACT FROM AUTHOR]