Abstract: For the past decade, the usage of d-dimensional databases in applications is considerably increasing in several fields like medicine, biology and CAD/CAM applications. The most popular research now running on is the retrieval of similar set of uncertain objects from the single-dimensional databases through indexing. However, for retrieving the relative set of uncertain objects from the database, searching plays a basic functionality. Several researchers implemented diverse set of approaches for similarity search so-called feature transformation. As a result, the similarity search is changed into a look for points in the feature space which are close to a particular query point in the multi-dimensional feature space. Numerous index structures and algorithms have been planned. It has been revealed that the process of presenting the novel index structures significantly develop the presentation in indexing the single-dimensional databases. In this research, researchers first made a focus on faster retrieval of data from single dimensional database. Contour-Cluster Vector (CCV) technique is formed by creating cluster that contains time series subsequences of approximately the same contour (shape). For each cluster, a lower-bound distance is computed for the users’ query and the most similar element of the cluster. CCV demonstration can be used as index for single dimensional data and that it permits more efficient similarity search. Simulation experiments conducted with a large number of instances per object to evaluate the efficacy of proposed CCV technique against indexing the multi-dimensional uncertain objects through range searching. The performance of the proposed CCV technique is evaluated in terms of processing time, number of instantiations and throughput and the evaluation results showed that it achieved 10-12% high in attaining the retreival of data.