Abstract: Inductive learning with modular classification rules is a kind of widely used algorithms in machine learning. It has developed in various aspects and branches, and one of its modern representatives is the Prism family of algorithms. The Prism family of algorithms has been applied in many kinds of inductive learning tasks after more than twenty years development, and it has become a major competitor to the induction of decision trees. This paper gives a survey on the Prim family of algorithms that run independently and simultaneously, respectively, based on the basic framework of Prism algorithms. The research has made a comparison study on multiple algorithms, with respect to their suitability, advantages and interrelation in multiple classification tasks. Furthermore, the perspectives of modular classification inductive learning algorithms are presented.