Due to the well-known difficulties implied by manually building an ontology, machine-driven knowledge acquisition techniques—in particular in the field of ontology learning—are promoted by many ontology engineering methodologies as a feasible alternative to aid ontology engineers in this challenging process. Though the benefits of ontology learning are widely acknowledged, to date its systematic application is considerably constricted by the lack of adequate methodological support. The advantages of an elaborated ontology learning methodology are twofold; on the one hand it reduces the need for a high expertise level in this field: a detailed description of the process and best practices in operating it in a variety of situations make ontology learning techniques more accessible to large communities of ontology developers and users; on the other hand the methodology clearly formalizes the ways ontology learning results are integrated into a more general ontology engineering framework, thus opening up new application scenarios for these techniques and technologies. In this article we aim at contributing at the operationalization of ontology learning processes by introducing a methodology describing the major coordinates of these processes in terms of activities, actors, inputs, outputs and support tools. The methodology was employed to build an ontology in the legal domain. We present the lessons learned from the case study, which are used to empirically validate the proposed process model.