Kernel-based methods have been widely used in various machine learning tasks. The performance of these methods strongly relies on the choice of the kernel which represents the similarity between each pair of data points. Therefore, choosing an appropriate kernel function or tuning its parameter(s) becomes an important issue in the kernel-based methods. Multiple Kernel Learning (MKL) methods have been developed to tackle this problem by learning an optimal combination of a set of predefined kernels. Distance Metric Learning (DML) approaches have been also attracted the attention of a number of researchers in order to find an optimum metric automatically. In this paper, within the framework of the SVM classifier, we present a MKL method which is based on the concept of the distance metric learning theory. The method is then compared to the other popularly used MKL approaches. It is shown that those approaches which iteratively optimise the SVM and the MKL parameters usually outperform the others.