While storytelling has long been recognized as an important part of effective knowledge management in organizations, knowledge management technologies have generally not distinguished between stories and other types of discourse. In this paper we describe a new type of technological support for storytelling that involves automatically capturing the stories that people tell to each other in conversations. We describe our first attempt at constructing an automated story extraction system using statistical text classification and a simple voting scheme. We evaluate the performance of this system and demonstrate that useful levels of precision and recall can be obtained when analyzing transcripts of interviews, but that performance on speech recognition data is not above what can be expected by chance. This paper establishes the level of performance that can be obtained using a straightforward approach to story extraction, and outlines ways in which future systems can improve on these results and enable a wide range of knowledge socialization applications.