The OLMAM algorithm (Optimized Levenberg- Marquardt with Adaptive Momentum) is a variant of the Levenberg-Marquardt algorithm for training multi- layer feedforward neural networks. OLMAM has been shown to obtain excellent solutions in difficult classifi- cation problems where other computational intelligence techniques usually achieve inferior performances. In this paper we apply OLMAM to the problem of author identification of e-mail messages which is a challenging classification problem due to the special characteristics of the data. We performed a number of experiments with a corpus of real-world e-mail messages (Enron corpus). The performance of the proposed method was compared with the performances achieved by Naive-Bayes and SVM classifiers. Author identification with OLMAM was found to be significantly better compared with the other methods even if the author wrote about different topics.