Temporal expressions are one of the important structures in natural language. In order to understand text, temporal expressions have to be identified and normalized by providing ISO-based values. In this paper we present a shallow approach for automatic recognition of temporal expressions based on a supervised machine learning approach trained on an annotated corpus for temporal information, namely TimeBank. Our experiments demonstrate a performance level comparable to a rule-based implementation and achieve the scores of 0.872, 0.836 and 0.852 for precision, recall and F1-measure for the detection task respectively, and 0.866, 0.796, 0.828 when an exact match is required.status: publishe