Intrusion detection is an effective mechanism to dealing with the attacks in computer networks. Pattern recognition techniques have been used for network intrusion detection for more than a decade. Almost all of such intrusion detection systems (IDSs) use an individual classifier to distinguish normal behavior patterns from attack signatures. Moreover these systems have a high false alarm rate and high cost. In this paper, a hierarchical classifier combiner is proposed to detect network intrusions based on the fusion of multiple well-known and efficient classifiers. The KDDCUP99 dataset is used to train and test the classifiers. The overall performance in terms of the overall error rate, average cost and the false alarm rate is investigated and discussed. Also, the performance of the proposed approach is compared with the performance of the most common non- hierarchical combination approaches as well as individual classifiers.