An extended version of Kohonen's Learning Vector Quantization (LVQ) al
gorithm, called Distinction Sensitive Learning Vector Quantization (DS
LVQ), is introduced which overcomes a major problem of LVQ, the depend
ency on proper pre-processing methods for scaling and feature selectio
n. The algorithm employs a weighted distance function and adapts the m
etric with learning. Highest weights are assigned to components in the
input vectors which are most informative for classification; non-info
rmative components are discarded. The algorithm is applied to the anal
yses of multi-channel EEG data and compared with experienced methods.