This paper provides an introduction to Natural Language Interfaces to Databases (NLIDBS), discussing their history, advantages, disadvantages, and key methodologies. It highlights the linguistic challenges NLIDBS face and explores various architectural approaches, including pattern-matching, syntax-based, semantic grammar, and intermediate representation languages. The paper also addresses portability issues, restricted natural language input systems, reasoning capabilities, and less explored areas such as database updates, meta-knowledge questions, temporal questions, and multi-modal interfaces. Finally, it reflects on the current state of the art in NLIDBS. The authors emphasize that while NLIDBS offer advantages like natural language support and better handling of certain types of queries, they also have limitations, such as unclear linguistic coverage, tedious configuration, and the need for specialized training. The paper concludes by noting the ongoing evolution of NLIDBS and their integration with advancements in natural language processing and reasoning technologies.This paper provides an introduction to Natural Language Interfaces to Databases (NLIDBS), discussing their history, advantages, disadvantages, and key methodologies. It highlights the linguistic challenges NLIDBS face and explores various architectural approaches, including pattern-matching, syntax-based, semantic grammar, and intermediate representation languages. The paper also addresses portability issues, restricted natural language input systems, reasoning capabilities, and less explored areas such as database updates, meta-knowledge questions, temporal questions, and multi-modal interfaces. Finally, it reflects on the current state of the art in NLIDBS. The authors emphasize that while NLIDBS offer advantages like natural language support and better handling of certain types of queries, they also have limitations, such as unclear linguistic coverage, tedious configuration, and the need for specialized training. The paper concludes by noting the ongoing evolution of NLIDBS and their integration with advancements in natural language processing and reasoning technologies.