Named Entity Recognition (NER) is the task of processing text to identify and classify names, which is an important component in many Natural Language Processing (NLP) applications, enabling the extraction of useful information from documents. Basically NER is a two step process and used for many application like Machine Translation. Indian languages are free order, and highly inflectional and morphologically rich in nature. In this paper we describe the various approaches used for NER and summery on existing work done in different Indian Languages (ILs) using different approaches and also describe brief introduction about Hidden Markov Model And the Gazetteer method for NER. We also present some experimental result using Gazetteer method and HMM method that is a hybrid approach. Finally in the last the paper also describes the comparison between these two methods separately and then we combine these two methods so that performance of the system is increased.