Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause radical changes in the concepts. This paper introduces a method for incremental concept learning in dynamic environments where the target concepts may be context-dependent and may change drastically over time. The method has been implemented in a system called FLORA3. FLORA3 is very flexible in adapting to changes in the target concepts and tracking concept drift. Moreover, by explicitly storing old hypotheses and re-using them to bias learning in new contexts, it possesses the ability to utilize experience from previous learning. This greatly increases the system's effectiveness in environments where contexts can reoccur periodically. The paper describes the various algorithms that constitute the method and reports on several experiments that demonstrate the flexibility of FLORA3 in dynamic environments.