Hierarchical Task Networks (HTNs) are a common formalism for automated planning, allowing to leverage the hierarchical structure of many activities. While HTNs have been used in many practical applications, building a complete and efficient HTN model remains a difficult and mostly manual task. In this paper, we present an algorithm for learning such hierarchical models from a set of demonstrations. Given an initial vocabulary of tasks and accompanying demonstrations of possible ways to achieve them, we present how each task can be associated with a set of methods capturing the knowledge of how to achieve it. We focus on the algorithms used to learn the structure of the model and to efficiently parameterize it, as well as an evaluation in terms of planning performance.