Many linear statistical models have been lately proposed in text classification related literature and evaluated against the Unsolicited Bulk Email filtering problem. Despite their popularity - due both to their simplicity and relative ease of interpretation - the non-linearity assumption of data samples is inappropriate in practice, due to its inability to capture the apparent non-linear relationships, which characterize these samples. In this paper, we propose the SF-HME, a Hierarchical Mixture-of-Experts system, attempting to overcome limitations common to other machine- learning based approaches when applied to spam mail classification. By reducing the dimensionality of data through the usage of the effective Simba algorithm for feature selection, we evaluated our SF-HME system with a publicly available corpus of emails, with very high similarity between legitimate and bulk email - and thus low discriminative potential - where the traditional rule based filtering approaches achieve considerable lower degrees of precision. As a result, we confirm the domination of our SF-HME method against other machine learning approaches, which appeared to present lesser degree of recall.