Kernel methods have become a standard tool for pattern analysis during the last fifteen years since the introduction of support vector machines. We will introduce the key ideas and indicate how this approach to pattern analysis enables a relatively easy plug and play application of different tools. The problem of choosing and designing a kernel for specific types of data will also be considered and an overview of different kernels will be given.