We study visual quality control in the ceramics industry. In tile manufacturing, it is important that in each set of tiles, every single tile looks similar. Currently, the estimation is usually done by human vision. Our goal is to design a machine vision system that can estimate the sufficient similarity, or same appearance, to the human eye. Our main approach is to use accurate spectral representation of color, and compare spectral features to the rGb color features. A laboratory system for color measurements is built. Experimentations with five classes of brown tiles are presented and discussed. In addition to the k-nearest neighbor (k-NN) classifier, a neural network called the self-organizing map (SOM) is used to provide understanding of the spectral features. Every single spectrum in each tile of a training set is used as input to a 2-D sOm. The SOM is analyzed to understand how spectra are clustered. As a result, tiles are classified using a trained 2-D SOM. It is also of interest to know whether the order of spectral colors can be determined. In our approach, all spectra are clustered in a 1-D SOM, and each pixel (spectrum) is presented by pseudocolors according to the trained nodes. Finally, the results are compared to experiments with human vision.