This paper introduces natural language interfaces to databases (NLIDBs), discussing their history, advantages, disadvantages, linguistic challenges, and current research. NLIDBs allow users to query databases using natural language, such as English, instead of formal query languages or graphical interfaces. The paper reviews the evolution of NLIDBs from the 1960s to the 1980s, highlighting key systems like LUNAR, RENDEZVOUS, LADDER, CHAT-80, and JANUS. It discusses the challenges of NLIDBs, including linguistic ambiguity, modifier attachment, quantifier scoping, and anaphora resolution. The paper also covers NLIDB architectures, portability issues, restricted natural language input systems, and systems with reasoning capabilities. It highlights less explored areas such as database updates, meta-knowledge questions, temporal questions, and multi-modal interfaces. The paper concludes that while NLIDBs have potential, they face challenges in commercial adoption due to their complexity and the availability of more user-friendly alternatives like graphical interfaces. Current research focuses on improving NLIDBs through advances in natural language processing, integrating language and graphics, and developing reasoning modules. The paper also reviews commercially available NLIDBs and their capabilities, emphasizing the importance of understanding the user's intent and the limitations of natural language in database queries.This paper introduces natural language interfaces to databases (NLIDBs), discussing their history, advantages, disadvantages, linguistic challenges, and current research. NLIDBs allow users to query databases using natural language, such as English, instead of formal query languages or graphical interfaces. The paper reviews the evolution of NLIDBs from the 1960s to the 1980s, highlighting key systems like LUNAR, RENDEZVOUS, LADDER, CHAT-80, and JANUS. It discusses the challenges of NLIDBs, including linguistic ambiguity, modifier attachment, quantifier scoping, and anaphora resolution. The paper also covers NLIDB architectures, portability issues, restricted natural language input systems, and systems with reasoning capabilities. It highlights less explored areas such as database updates, meta-knowledge questions, temporal questions, and multi-modal interfaces. The paper concludes that while NLIDBs have potential, they face challenges in commercial adoption due to their complexity and the availability of more user-friendly alternatives like graphical interfaces. Current research focuses on improving NLIDBs through advances in natural language processing, integrating language and graphics, and developing reasoning modules. The paper also reviews commercially available NLIDBs and their capabilities, emphasizing the importance of understanding the user's intent and the limitations of natural language in database queries.