Classification is a very well-studied task in data mining. In the last years, important works have been published to scale up classification algorithms in order to handle large datasets. However, due to the high rate of streams of data, a number of emerging applications are demanding new approaches. Rule learning is an efficient alternative to address non-stationary environments. The talk presents an overview of rule-based learning algorithms for data streams and emphasizes some important aspects of these techniques.