Learning from Demonstration is an effective method for interactively teaching skills to a robot learner. However, a skill learned via demonstrations is often learned within a particular environment and uses a specific set of objects, and thus may not be immediately applicable for use in unfamiliar environments. Transfer learning addresses this problem by enabling a robot to apply learned skills to unfamiliar environments. We describe our ongoing work to develop a system which enables transfer learning by representing skill demonstrations according to the level of similarity between the source and target environments.