Accurate identification of DNA regulatory elements becomes an urgent need in the post-genomic era. Recent genome-wide chromatin states mapping efforts revealed that DNA elements are associated with characteristic chromatin modification signatures, based on which several approaches have been developed to predict transcriptional enhancers. However, their practical application is limited by incomplete extraction of chromatin features and model inconsistency for predicting enhancers across different cell types. To address these issues, we define a set of non-redundant shape features of histone modifications, which shows high consistency across cell types and can greatly reduce the dimensionality of feature vectors. Integrating shape features with a machine-learning algorithm AdaBoost, we developed an enhancer predicting method, DELTA (Distal Enhancer Locating Tool based on AdaBoost). We show that DELTA significantly outperforms current enhancer prediction methods in prediction accuracy on different datasets and can predict enhancers in one cell type using models trained in other cell types without loss of accuracy. Overall, our study presents a novel framework for accurately identifying enhancers from epigenetic data across multiple cell types.