This paper presents a multi-view gait recognition algorithm for identification at a distance. We make use of two well known and effective gait representations namely Motion Silhouette Image (MSI) and Gait Energy Image (GEI). MSI and GEI inherently capture the spatiotemporal characteristics of gait. We show that the individual recognition performance of MSI and GEI can be improved by using a fusion methodology. The features for MSI and GEI images are extracted using Independent Component Analysis (ICA) which is used widely in such applications. Extreme Learning Machine (ELM) classifier is then used for classification. ELM is a multiclass classifier which offers the advantage of less time consumption and high performance. The results are fused at score level making use of fusion rules such as min and max  to make the algorithm robust, reliable and to improve the performance of the system. Our approach is tested on the NLPR gait database. The NLPR gait database corresponds to 20 subjects, each subject has 4 sequences and there are 3 viewing angles (0°, 45° and 90°) for each person. The results on the dataset show that the fusion gives good performance for the 3 views considered in this paper.