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It is used in a variety of practical applications. See the [http://liinwww.ira.uka.de/bibliography/Neural/SOM.LVQ.html 'Bibliography on the Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ)]'.
A key issue in LVQ is the choice of an appropriate measure of distance or similarity for training and classification. Recently, techniques have been developed which adapt a parameterized distance measure in the course of training the system, see e.g. (Schneider, Biehl, and Hammer, 2009)<ref>{{cite journal|authors=P. Schneider, B. Hammer, and M. Biehl|title=Adaptive Relevance Matrices in Learning Vector Quantization|journal= Neural Computation|volume=21|issue=10|pages=3532–3561|year=2009|doi=10.1162/neco.2009.10-08-892|pmid=19635012|citeseerx=10.1.1.216.1183|s2cid=17306078}}</ref> and references therein.
LVQ can be a source of great help in classifying text documents.{{Citation needed|date=December 2019|reason=removed citation to predatory publisher content}}
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