This book focuses on the automatic identification of musical piece versions (alternate renditions of the same musical composition like cover songs, live recordings, remixes, etc.). In particular, two core approaches for version identification are proposed: model-free and model-based. Furthermore, the book introduces the use of post-processing strategies to improve the identification of versions in a query-by-example paradigm. Overall, several tools and concepts are employed, including nonlinear signal analysis, complex networks, and time series models. This work brings automatic version identification to an unprecedented stage where high accuracies are achieved and, at the same time, explores promising directions for future research. Although the main steps are guided by the nature of the considered signals (music recordings) and the characteristics of the task at hand (version identification), the methodology of this book can be easily transferred to other contexts and domains.