In this paper, we propose a multi-criteria based active learning approach and effectively apply it to named entity recognition. Active learning targets to minimize the human annotation efforts by selecting e xamples for labeling. To maximize the contribution of the selected examples, we consider the multiple criteria: informativeness, representativeness and diversity and propose measures to quantify them.