Data mining techniques are used in many areas in the world to retrieve the useful knowledge from the very large amount of data. Sequence pattern mining is the important techniques in data mining concepts with the wide range of applications. The applications of the sequence patterns data mining are weblog click streams, DNA sequences, sales analysis, telephone calling patterns, stock markets and etc., The methods for sequential pattern mining are categorised in to two approached. First approach is Apriori-based approach and second is Pattern-Growth-based approaches. In this paper, a methodical review of the sequential pattern mining algorithms is accomplished. Finally, reasonablestudy is done on the base of important key features reinforced by many algorithms and current research encounters are discoursed in this area of data mining. In this paper, an organized survey of the sequential pattern mining algorithms is accomplished. This paper examines these algorithms by studying the classification algorithm for sequential pattern-mining. These algorithms classified into two extensive classes. First, on the foundation of algorithms which are considered to surge effectiveness of mining and the other, on the origin of numerous additions of sequential pattern mining planned for certain application.At the end, comparative analysis is done on the basis of important key features supported by various algorithms and current research challenges are discussed. © 2015 Elsevier B.V., All rights reserved.