Syllabification is an essential component of many speech and language processing systems. The development of automatic speech recognizers frequently requires working with subword units such as syllables. More importantly, syllabification is an inevitable part of speech synthesis system. In this paper we present data-driven approaches to supervised learning and automatic detection of syllable boundaries. The generalization capability of the learning is investigated on the assignment of syllable boundaries to phoneme sequence representation in English. A rule-based self-correction algorithm is also proposed to automatically correct some syllabification errors. We conducted a series of experiments and the neural network approach is clearly better in terms of generalization performance and complexity.