When learning about the world, we develop mental representations or concepts for things we have never seen. At the same time, we also develop representations for things that are similar to what we have experienced. Traditionally, similarity-based and rule-based systems have been used as distinct models to capture conceptual representation. However, it seems implausible that we do not flexibly deploy both systems. Whether both systems can be used simultaneously to represent components of a single concept is an open empirical question. One example suggesting that the use of both systems is possible is the concept of a ZEBRA , which looks like a horse but striped. Using an artificial concept learning task, we test whether people can combine similarity and rules compositionally in order to represent concepts. Our results suggest that people are able to compose similarity and rules when mentally representing a single concept.