l neural network models have been developed to address the classification problem. Usually, one makes the distinction between supervised and unsupervised classifiers: A supervised classifier is used , when an analyst has some examples, for which the correct classification is known. This can be done, for example, in most problems related to particle physics at accelerators, where there is a generally good knowledge of detectors and of the underlying physics, and good simulations are available. Whereas in an unsupervised technique, the events are partitioned into classes of similar elements, without using additional information. This is the case especially for fields operating in a discovery regime, as, e.g., astroparticle physics [@ale].

From a mathematical perspective, a neural network is simply a mapping from $R^n \rightarrow R^m$, where $R^n$ is the input data set dimension and $R^m$ is the output dimension of the neural network . The network is typically divided into various layers; each layer has a set of neurons also called as nodes or information units, connected together by the links. The artificial neural networks are able to classify data by learning to discriminate patterns in features (or parameters) associated with the data. The neural network learns from the data set when each data vector from the input set is subjected to it. The learning or information gain is stored in the links associated with the neurons.

The output generated by the network depends on both the problem and network type. For the gamma/hadron separation problem the supervised network maps each input vector onto the \[0,1\] interval, whereas in unsupervised networks the nodes are adapted to the input vector in such a way that the output of the network represents the natural groups that exist in the data set. A visualization technique is used to view the groups discovered by the network.

Section 2 describes the data sets used for the classification. Section 3 deals with the multilayer perceptron network and its classification res