The persistence and evolution of systems essentially depend of their ability to self-adapt to new situations. As an expression of intelligence, adaptation is a distinguishing quality of any system that is able to learn and to adjust itself in a flexible manner to new environmental conditions. Such ability ensures selfcorrection over time as new events happen, new input becomes available, or new operational conditions occur. This requires self-monitoring of the performance in an ever changing environment. The relevance of adaptation is established in numerous domains and by versatile real world applications.The primary goal of this contribution is to investigate adaptation issues in learning classification systems formdifferent perspectives. Being a scheme of adaptation, life long incremental learning will be examined. However, special attention will be given to adaptive neural networks and the most visible incremental learning algorithms (fuzzy ARTMAP, nearest generalized exemplar, growing neural gas, generalized fuzzy minmax neural network, IL based on function decomposition) and their adaptation mechanisms will be discussed. Adaptation can also be incorporated in the combination of such incremental classifiers in different ways so that adaptive ensemble learners can be obtained too. These issues and other pertaining to drift will be investigated and illustrated by means of a numerical simulation.