Statutory reasoning is the task of determining how laws apply to a legal case. This is a basic skill for lawyers, and in its computational form, a fundamental task for legal artificial intelligence systems. In this talk, I describe initial steps towards solving computational statutory reasoning. First, I define this task in the context of legal practice, and artificial intelligence more broadly. Second, I introduce the StAtutory Reasoning Assessment benchmark dataset (SARA). With the ability to measure performance on statutory reasoning, I show how a symbolic system can solve the task, while state-of-the-art machine reading struggles. Third, I focus on the facts described in the SARA cases: I revise the symbolic solver’s ontology and introduce models for information extraction. The attained performance opens up new perspectives on how to solve statutory reasoning.