One of the challenging tasks in machine learning is the classification of time series. It is not very different from standard classification except that the time shifts across time series should be corrected by using a suitable alignment algorithm. In this study, we proposed a framework designed for distance based time series classification which enables users to easily apply different alignment and classification methods to different time series datasets. The framework can be extended to implement new alignment and classification algorithms. Using the framework, we implemented the k-Nearest Neighbor and Support Vector Machines classifiers as well as the alignment methods Dynamic Time Warping, Signal Alignment via Genetic Algorithm, Parametric Time Warping and Canonical Time Warping. We also evaluated the framework on UCR time series repository for which we can conclude that a suitable alignment method enhances the time series classification performance on nearly every dataset. (C) 2015 Elsevier Ltd. All rights reserved.