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It has recently been shown that artificial immune systems (AIS) can be successfully used in many machine learning tasks. The aiNet, one such AIS algorithm exploiting the biologically-inspired features of the immune system, performs well on elementary clustering tasks. This paper proposes the use of the aiNet to more complex tasks of document clustering. Based on the immune network and affinity maturation principles, the aiNet performs an evolutionary process on the raw data, which removes data redundancy and retrieves good clustering results. Also, Principal Component Analysis is integrated into this method to reduce the time complexity. The results are compared with some classical document clustering methods -Hierachical Agglomerative Clustering and K-means.