In this paper, a multilayered clustering framework is proposed to build a service portfolio to select web services of choice. It is important for every service provider to create a service portfolio in order to facilitate the service selection process for someone to obtain the desired service in the absence of public UDDI registries. To address this problem, a multilayered clustering approach applied on a variety of data pertaining to web services in order to filter and group the services of a similar kind which in turn will improve the leniency in the process of service selection is used. The advantages of the layer approach are reduced search space, combination of incremental learning and competitive learning strategies, reduced computational labour, scalability, robustness and fault tolerance. The results are subjected to cluster analysis to verify their degree of compactness and isolation and appropriate evaluation indices are used. The results were found passable with an improved degree of similarity.