Cultural assets are all movable and immovable assets that have been the subject of social life in historical periods, have unique scientific and cultural value, and are located above ground, underground or underwater. Today, the fact that most of the analyses conducted to understand the technologies of these assets require sampling and that non-destructive methods that allow analysis without taking samples are costly is a problem for cultural heritage workers. In this study, which was prepared to find solutions to national and international problems, it is aimed to develop a non-destructive, cost-minimizing and easy-to-use analysis method. Since this article aimed to develop methodology, the materials were prepared for preliminary research purposes. Therefore, it was limited to four primary colors. These four primary colors were red and yellow ochre, green earth, Egyptian blue and ultramarine blue. These pigments were used with different binders. The produced paints were photographed in natural and artificial light at different light intensities and brought to a 256 × 256 pixel size, and then trained on support vector machine, convolutional neural network, densely connected convolutional network, residual network 50 and visual geometry group 19 models. It was asked whether the trained VGG19 model could classify the paints used in archaeological and artistic works analyzed with instrumental methods in the literature with their real identities. As a result of the test, the model was able to classify paints in artworks from photographs non-destructively with a 99% success rate, similar to the result of the McNemar test.