Utilizing a SOM is not limited to its training data. If similar data can be described with the same feature vectors, it can also be processed by an already-trained SOM. Again, the Euclidean distances from this data to the nodes of the map can be computed and, as a result, the data gets sorted on the trained map. This reveals similarities and differences between the new data vectors but also between the new and the training data.

This works especially well if similar data is used to train the map. Otherwise, the distances to all of the map's nodes are rather large. There is always one node, however, that is the closest. If, e.g., the SOM was trained using only different shades of blue, it may distinguish them very well but might have problems distinguishing between red and green. On the other hand, if a SOM was trained with many different colors, it can roughly distinguish between all kinds of arbitrary colors but struggles in very precise separation of slightly differing shades.