In this paper, we propose a model for feature selection and indexing of online signatures based person identification. For representation of online signatures, a set of 100 global features of MCYT online signature database is considered. However, MCYT based features are high dimension features which significantly increases the response time and space requirements for signature identification process. To overcome this problem, multi cluster feature selection method is proposed to reduce the dimensionality by finding a relevant feature subset. Moreover, in some applications, where the database is supposed to be very large, the identification process typically has an unacceptably long response time. A solution to speed up the identification process is to design an indexing model prior to identification which reduces the number of candidate hypotheses to be considered during matching by the identification algorithm. Hence in this paper, Kd-tree based indexing model is designed for online signatures based person identification. The experimental results reveal that the proposed model works more efficiently both in terms of time and accuracy.