The node of the map that has the smallest Euclidean distance to a training item is called the best matching unit (BMU). Once a BMU of a specific item is found, the map adapts to it. The training item drags this node's pointer toward the location of itself. This means that the pointer is modified to become a weighted mean value of the item's location and the original pointer. Put more simply, the color of the BMU is slightly changed towards the color of the training item. The weighting is called the learning coefficient and it usually decreases over time. The pointers of all neighboring nodes are modified, too. The larger the distance between a node and the winning node, the lower the learning coefficient.

We can easily visualize the result: after the first learning step the color of the map changes and the training items change their BMU. This process is repeated many times. Finally, when no changes can be detected, the training is over. Now the map has different regions assigned to different colors. In between those regions there may be smooth or sudden transitions, depending on the training data.