Abstract
The family of artificial neural networks based on Adaptive Resonance Theory (ART) forms a collection of distinct mathematical pattern recognition methods. The classification of sensor signals, process data analysis, spectral interpretation, and image analysis are discussed as applications of ART outside and within chemistry. The advantages of ART are considered. They include its use as a built-in detector for outliers, its rapid training speed, self-organizational behaviour, full chemical interpretability, and real-time and on-line applicability. A glossary of terms used in ART is given at the end of the article.
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