This paper explores basic aspects of the immune system and proposes a novel immune

network model with the main goals of clustering and filtering redundant data from

problems described by a set of discrete samples. It is not our concern to reproduce with

confidence any immune phenomenon, but to show that immune concepts can be used to

develop novel computational tools for data processing. As important results of our

model, the network evolved will be capable of reducing redundancy, describing data

structure, shapes and their cluster inter-relations. The data clustering approach will be

will be implemented in association with a statistical technique, and the network

performance will be illustrated using two benchmark problems. The paper is concluded

with a trade-off between the proposed network and artificial neural networks.