Today, a huge amount of tables are presented in web pages, word documents, and spreadsheets. Many of them are unstructured tabular data. They are intended to be understood by humans but not to be interpreted by machines. At the same time, we often need to have that information in a structured form, e.g. relational databases. We propose a rule-based approach to table analysis and interpretation and demonstrate how it can be applied to transform tabular data from unstructured (spreadsheets) to structured (relational databases) form. The paper discusses representing tabular data as facts in the working memory of a rule engine, a formal language for defining rules of table analysis and interpretation, and its implementation.