A neural classifying system is developed to identify three particle classes in experimental high-energy physics. The system makes use of the extraction of principal discriminating components to obtain compactness and high classification efficiency, even identifying outsiders in experimental data sets. More than 97% of analysed events are correctly classified.
Neural Networks; Pattern Recoginition; Preprocessing
