Color specification is the process of measuring the color of a sample in a given color space. We focused onto the Munsell color space as archaeologists are used to employ the so called Munsell Soil Color Charts (MSCCs) directly in the excavation sites. For these scholars and researchers, being enabled to perform Munsell color specification in an automatic way is crucial, as they spend a lot of time to subjectively specify colors in the Munsell system. We extended the dataset ARCA328, which was specifically thought for the automatic Munsell color specification issue, increasing the number of images from 328 to 1,488, and the number of samples from 56,160 to 315,333. Then, we conducted generalization-tests of color conversion for color specification, adopting a classification approach instead of a regression one. This choice was motivated by the fact that the set of all the possible HVC coordinates in the MSCCs is a discrete one. Hence, we decided to consider each chip in the MSCCs as a class to be learnt and recognized by the SVC. With these tests we reached the limits of automatic Munsell color specification without any reference-system or calibration phase. Finally, we gave insights for future works aimed to design automatic illuminant calibration phase and to investigate deep learning approaches, leveraging a synthetic images rendering procedure we also present in this work.