A symbolic image database system is a system in which a large amount of image data and their related information are represented by both symbolic images and physical images. How to perceive spatial relationships among the components in a symbolic image is an important criterion to find a match between the symbolic image of the scene object and the one being store as a modal in the symbolic image database. Spatial reasoning techniques have been applied to pictorial database, in particular those using 2D strings as an index representation have been successful. In this thesis, we extend the existing three levels of type-i similarity to more levels to aid similarity retrieval more precisely. There are 13 spatial operators which were introduced by Lee and Hsu to completely represent spatial relationships in 1D space. But, they just combined the 13 spatial relationships on x- and y-axis to represent the spatial relationships in 2D space by 13 times 13 = 169 spatial relationships. However, the 169 spatial relationships are still not sufficient to show all kinds of spatial relationships between any two objects in 2D space. For example, the directional relationships, like North or South West, exist in 2D space and is difficult to be deducted from those 13 spatial operators. Thus, we add the nine directional relationships to the 169 spatial relationships in 2D space. In this way, we can distinguish up to 289 spatial relationships in 2D space. Moreover, in our proposed strategy, we also take care of the problem caused by the MBRs. In most of the previous approaches for iconic indexing, for simplifying the concerns, they apply the MBRs of two objects to define the spatial relationship between them. The topological relationships, however, between objects can be quite different from the spatial relationship of their respective $MBR$s. Therefore, sometimes, it is hard to correctly describe the spatial relationship of the objects in terms of the relationships between their corresponding MBRs. To improve this drawback resulted from MBRs, we adopting the concept of topological relationships in our proposed strategy. Good access methods for large image databases are important for efficient retrieval. The signature files can be viewed as a preselection searching filter to prune off the unsatisfied images. In order to solve the ambiguity of the MBRs and to present the spatial relationships in two dimensional space completely, we propose a hybrid approach-based signature extraction method for similarity retrieval. From our simulation study, we show that our approach can provide a higher rate of a correct match and requires a smaller storage cost than Lee et al.'s 2D B-based signature approach. In some case, the correct match rate based on our proposed strategy can be up to 42.18%, while it is just 16.66% in Lee et al.'s strategy. Moreover, the worst case of the storage cost required in our proposed strategy is 1686 bits. But, it always needs 2015 bits in Lee et al.'s strategy.