Quotations in a text document contain important information about the content, the context, the sources that the author uses, their importance and impact. Therefore, automatic identification of quotations from documents is an important task. Quotations included in rabbinic literature are difficult to identify and to extract for various reasons. The aim of this research is to automatically identify Biblical quotations included in rabbinic documents written in Hebrew-Aramaic. We deal with various kinds of quotations: partial, missing and incorrect. We formulate nineteen features to identify these quotations. These features were divided into seven different feature sets: matches, best matches, sums of weights, weighted averages, weighted medians, common words, and quotation indicators. Several features are novel. Experiments on various combinations of these features were performed using four common machine learning methods. A combination of 17 features using J48 (an improved version of C4.5) achieves an accuracy of 91.2%, which is an improvement of about 8% compared to a baseline result.