Over the last decade, microarrays have become a fundamental tool in biological research laboratories throughout the world. During this time, methods for performing microarray experiments have improved and expanded rapidly, creating an enormous demand for evaluation and comparison of emerging and existing technologies. Importantly, the responsibility for doing this lies as much with the data analyst as the data generator. Such evaluations are dif cult since they are in uenced by many factors, both nancial and scienti c. They require a good understanding of both the biological underpinnings of new array technologies and their applications, as well as the statistical issues involved when analyzing the resulting data. To date, there have been many empirical comparisons of technologies for expression array pro ling, but newer applications are still lagging in this respect. With the growth in interest in applying microarrays to study a different aspect of the genome, namely the epigenome, this problem has again come to the fore. While there are many publications exploring the biology of DNA methylation and the epigenome, and a large number of articles describing the development of approaches for studying DNA methylation, there are few articles that address the analytic issues involved in these new experiments. This chapter aims to address this problem. It is aimed at the biologist who wants to understand the limitations in analyzing data obtained from different DNA methylation arrays, and the computational biologist wanting an entry point into this new and exciting area.