In this work, microRaman spectroscopy is used to investigate the stratigraphic mapping in paintings. The objective of mapping imaging is to segment the dataset, here spectra, into clusters each of which consisting spectra that have similar characteristics; hence, similar chemical composition. The spatial distribution of such clusters can be illustrated in pseudocolor images, in which each pixel of image is colored according to its cluster membership. Such mapping images convey information about the spatial distribution of the chemical substances in an object. Moreover, the laser light source that is used has excitation in 1064 nm, i.e., near infrared (NIR), allowing the penetration of the radiation in deeper layers. Thus, the mapping images that are produced by clustering the acquired spectra (specifying specific bands of Raman shifts) can provide stratigraphic information in the mapping images, i.e., images that convey information of the distribution of substances from deeper, as well. To cluster the spectra, unsupervised machine learning algorithms are applied, e.g., hierarchical clustering. Furthermore, the optical microscopy camera (×50), where the Raman probe (B and WTek iRaman EX) is plugged in, is attached to a computerized numerical control (CNC) system which is driven by a software that is specially developed for Raman mapping. This software except for the conventional CNC operation allows the user to parameterize the spectrometer and check each and every measurement to ensure proper acquisition. This facility is important in painting investigation because some materials are vulnerable to such specific parameterization that other materials demand. The technique is tested on a portable experimental overpainted icon of a known stratigraphy. Specifically, the under icon, i.e., the wavy hair of “Saint James”, can be separated from upper icon, i.e., the halo of Mother of God in the “Descent of the Cross”.