The basic Self-Organizing Network can be visualized as a sheet-like neural-network array , the cells (or nodes) of which become specifically tuned to various input signal patterns or classes of patterns in an orderly fashion. The learning process is competitive and unsupervised, meaning that no teacher is needed to define the correct output (or actually the cell into which the input is mapped) for an input. In the basic version, only one map node (winner) at a time is activated corresponding to each input. The locations of the responses in the array tend to become ordered in the learning process as if some meaningful nonlinear coordinate system for the different input features were being created over the network (Kohonen, 1995c).The SOM was developed by Prof. Teuvo Kohonen in the early 1980s. The first application area of the SOM was speech recognition, or perhaps more accurately, speech-to-text transformation. (Timo Honkela)