The increasing integration of technology in the different areas of science and industry has resulted in the design of applications that generate large amounts of data on-line. Most often, extracting information from these data is key, in order to gain a better understanding of the processes that the data are describing. Learning from these data poses new challenges to traditional machine learning techniques, which are not typically designed to deal with data in which concepts and noise levels may vary over time. The purpose of this paper is to present supervised neural constructivist system (SNCS), an accuracy-based neural-constructivist learning classifier system that makes use of multilayer perceptrons to learn from data streams with a fast reaction capacity to concept changes. The behavior of SNCS on data stream problems with different characteristics is carefully analyzed and compared with other state-of-the-art techniques in the field. This comparison is also extended to a large collection of real-world problems. The results obtained show that SNCS can function in a variety of problem situations producing accurate classification of data, whether the data are static or in dynamic streams.