Goal Recognition concerns the problem of determining an agent's final goal, deduced from the plan they are currently executing (and subsequently being observed). For over twenty years, the de facto standard in plan and goal recognition has been to map an agent's observations to a set of known, valid and sound plans held within a plan library. In this time many novel techniques have been applied to the recognition problem, but almost all have relied on the presence of a library in some form or another. The work presented in this thesis advances the state-of-the-art in goal recognition by removing the need for any plan or goal library. Such libraries are tedious to construct, incomplete if done by hand, and possibly contain erroneous or irrelevant entries when done by machine. This work presents a new formulation of the recognition problem based on planning, which removes the need for such a structure to be present. This greatly widens the scenarios in which goal recognition can be realistically performed. While this new formalism overcomes many of the problems associated with traditional recognition research, it remains compatible with many of the concepts found in previous recognition work. This new defnition is first defined in the context of a rational agent and observer, before several relaxations are introduced which enable tractable goal recognition. This relaxed implementation is then extensively evaluated with regard to multiple aspects of the recognition problem.