Named Entity Recognition (NER) is a process of identifying and categorizing all named entities in a document into predefined classes like person, organization, location, time, and numeral expressions. This identification and classification of proper names in text has recently considered as a major importance in natural language processing as it plays a significant role in various types of NLP applications, especially in information extraction, information retrieval, machine translation, and question-answering. This paper reports about the development of a NER system for Amharic using Conditional Random Fields (CRFs). Though this state of the art machine learning method has been widely applied to NER in several well-studied languages, this is the first attempt to use this method to Amharic language. The system makes use of different features such as word and tag context features, part of speech tags of tokens, prefix and suffix. Since feature selection plays a crucial role in CRF framework, experiments were carried out to find out most suitable features for Amharic NE tagging task.