It is noted that in a standard functional imaging analysis, in order to give model parameters, a statistical model is fitted to the data. The model parameters are then used to look for an effect. To do this, a statistic for each brain voxel is calculated that tests for the effect of interest in that voxel. The result is a large volume of statistic values. This is the multiple comparison problems in functional imaging. Random field theory is a recent branch of mathematics that can be used to solve this problem. This chapter focuses on this multiple comparison problem in functional imaging and the way it can be solved using random field theory (RFT). This chapter explains why spatial correlation in imaging data causes problems for the Bonferroni correction and introduces RFT as a solution. Finally, it discusses the assumptions underlying RFT and the problems that arise when these assumptions do not hold. This chapter can be useful to those with no specific expertise in mathematics or statistics.