Business Rules (BRs) are a critical artifact in the requirements elicitation phase of the software development life cycle. BRs describe the policies, procedures, and constraints, within which an organization must meet its business goals. BRs are often written in natural language and are found scattered across software requirement specifications and other project documents. Several taxonomies have been proposed for classification of BRs. In this paper, we utilize Ross’s BR classification schema and present an approach to automatically label and classify BRs along this schema. Our approach uses Data Programming (DP) for generating labeled training data needed for training two deep learning-based models namely Bi-LSTM with Attention and Bidirectional Encoder Representations from Transformers (BERT) to classify the BRs. We obtained an average labeling accuracy of 0.73 for all the BR classes using DP. We evaluated the approach on an industry-specific dataset comprising of BRs from various business information system domains. The highest F-score we obtained (0.69) was with a Bi-LSTM with attention-based model.