Visual quality control is an important application area of machine vision. In ceramics industry, it is essential that in each set of ceramic tiles every single tile looks similar, while considering e.g. color and texture. Our goal is to design a machine vision system that can estimate the sufficient similarity or same appearance to the human eye. Currently, the estimation is usually done by human vision. Our main approach is to use accurate spectral representation of color, and compare spectral features to the RGB color features. The authors have recently proposed preliminary methods and results for the classification of color features. In this paper the approach is developed further to cope with illumination effects and to take more advantage of spectral features more. Experiments with five classes of brown tiles are discussed. Besides the k-NN classifier, a neural network, called the Self-Organizing Map (SOM) is used for understanding spectral features. Every single spectrum in each tile is used as input to a 2-D SOM with 30 X 30 nodes or neurons. The SOM is analyzed in order to understand how spectra are clustered. As a result, the nodes are labeled according to the classes. Another interest is to know whether we can find the order of spectral colors. In our approach, all spectra are clustered by 32 nodes in a 1-D SOM, and each pixel (spectrum) is presented by pseudocolors according to the trained nodes. Thus, each node corresponds to one pseudocolor and every spectrum is mapped into one of these nodes. Finally, the results are compared to experiments with human vision.