In each and every natural language nouns play a very important role. A subcategory of noun is proper noun. They represent the names of person, location, organization etc. The task of recognizing the proper nouns in a text and categorizing them into some classes such as person, location, organization and other is called Named Entity Recognition. This is a very essential step of many natural language processing applications that makes the process of information extraction easier. Named Entity Recognition (NER) in most of the Indian languages has been performed using rule-based, supervised and unsupervised approaches. In this work our target language is Assamese, the language spoken by most of the people in North-Eastern part of India and particularly in Assam. In Assamese language, Named Entity Recognition has been performed using the rule based and suffix stripping based approaches. Supervised learning technique is more useful and can be easily adapted to new domains compared to rule based approaches. This paper reports the first work in Assamese NER using a machine learning technique. In this paper Assamese Named Entity Recognition is performed using Naïve Bayes classifier. Since feature extraction plays the most important role in getting better performance in any machine learning technique, in this work our aim is to put forward a description of a few important features related to Assamese NER and performance measure of the system using these features. © 2014 IEEE.