This project concerns conversational question answering (CQA), which is the task of answering a sequence of interrelated questions that occur during a conversation between a human and a computer. In contrast to answering unrelated single questions one question at a time, CQA is a more challenging task, due to the requirement of understanding a question in the context of previous questions and answers, as well as dealing with the effect of cascading errors when previous questions are incorrectly answered. We propose a novel approach using neural networks, which incorporates co-reference resolution to enable correct understanding of a subsequent question in the context of previous questions and answers.