This paper provides an overview of multidimensional access methods for spatial databases. It discusses the challenges of managing spatial data, which differ from traditional databases in that spatial data often involves complex, dynamic, and high-dimensional objects. The paper categorizes access methods into point access methods (PAMs) and spatial access methods (SAMs), and presents various techniques for efficiently searching and managing spatial data. Spatial data is often represented as points, lines, polygons, or higher-dimensional polyhedra. The paper discusses the need for efficient data structures that can handle dynamic data, support a wide range of operations, and maintain performance in the face of complex queries. It highlights the importance of spatial indexing, which allows for efficient retrieval of spatial data based on proximity, overlap, or other spatial relationships. The paper reviews several key data structures, including the k-d-tree, BSP-tree, and BD-tree, which are used for organizing and querying spatial data. It also discusses multidimensional hashing techniques, such as grid files and hash trees, which are designed to efficiently store and retrieve spatial data. The paper emphasizes the importance of balancing performance, space efficiency, and scalability in spatial access methods. The paper concludes with a discussion of the theoretical and experimental results of various access methods, highlighting their strengths and weaknesses in different scenarios. It underscores the need for flexible and robust access methods that can adapt to the diverse requirements of spatial data management. The paper serves as a comprehensive survey of the state of the art in multidimensional access methods for spatial databases.This paper provides an overview of multidimensional access methods for spatial databases. It discusses the challenges of managing spatial data, which differ from traditional databases in that spatial data often involves complex, dynamic, and high-dimensional objects. The paper categorizes access methods into point access methods (PAMs) and spatial access methods (SAMs), and presents various techniques for efficiently searching and managing spatial data. Spatial data is often represented as points, lines, polygons, or higher-dimensional polyhedra. The paper discusses the need for efficient data structures that can handle dynamic data, support a wide range of operations, and maintain performance in the face of complex queries. It highlights the importance of spatial indexing, which allows for efficient retrieval of spatial data based on proximity, overlap, or other spatial relationships. The paper reviews several key data structures, including the k-d-tree, BSP-tree, and BD-tree, which are used for organizing and querying spatial data. It also discusses multidimensional hashing techniques, such as grid files and hash trees, which are designed to efficiently store and retrieve spatial data. The paper emphasizes the importance of balancing performance, space efficiency, and scalability in spatial access methods. The paper concludes with a discussion of the theoretical and experimental results of various access methods, highlighting their strengths and weaknesses in different scenarios. It underscores the need for flexible and robust access methods that can adapt to the diverse requirements of spatial data management. The paper serves as a comprehensive survey of the state of the art in multidimensional access methods for spatial databases.