This paper presents an approach to determining colors of specks in an image taken from a pulp sample. The task is solved through color classification by an artificial neural network. The network is trained using possibilistic target values. The problem of post-processing of a pixelwise-classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbor of a pixel being analyzed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The experiments performed have shown that the color classification results correspond well with the human perception of colors of the specks.