Input selection is a crucial step for learning systems especially when in system modeling and identification the dataset is with a large number of variables, as a redundant input usually impairs the transparency of the underlying model and also increases the complexity of computation. The primary objective of input selection is to select the relevant inputs under the available information. This paper gives a brief review of some important issues and recent developments in the literature. ©2009 IEEE.