In this paper, we describe a technique t improve named entity recognition in a resource-poo language (Hindi) by using cross-lingual information We use an on-line machine translation system and separate word alignment phase to find the projection o each Hindi word into the translated English sentence We estimate the cross-lingual features using an Englis named entity recognizer and the alignment information We use these cross-lingual features in a support vecto machine-based classifier. The use of cross-lingua features improves F1 score by 2.1 points absolute (2.9 relative) over a good-performing baseline model.