  Multi-dimensional data classification is an important and challenging problem
in many astro-particle experiments. Neural networks have proved to be versatile
and robust in multi-dimensional data classification. In this article we shall
study the classification of gamma from the hadrons for the MAGIC Experiment.
Two neural networks have been used for the classification task. One is
Multi-Layer Perceptron based on supervised learning and other is
Self-Organising Map (SOM), which is based on unsupervised learning technique.
The results have been shown and the possible ways of combining these networks
have been proposed to yield better and faster classification results.
