Game maps are useful for human players, general-game-playing agents, and data-driven procedural content generation. ese maps are generally made by hand-assembling manually-created screenshots of game levels. Besides being tedious and error-prone, this approach requires additional e ort for each new game and level to be mapped. e results can still be hard for humans or computational systems to make use of, privileging visual appearance over semantic information. We describe a so ware system, Mappy, that produces a good approximation of a linked map of rooms given a Nintendo Entertainment System game program and a sequence of bu on inputs exploring its world. In addition to visual maps, Mappy outputs grids of tiles (and how they change over time), positions of non-tile objects, clusters of similar rooms that might in fact be the same room, and a set of links between these rooms. We believe this is a necessary step towards developing larger corpora of high-quality semantically-annotated maps for PCG via machine learning and other applications.