Markov chains serve as one of the most important methods in the application of probability theory to real-world models involving uncertainty. Markov chains are sequences of random variables in which the variable's future value depends on the variable's present value but is independent of the variable's history. This chapter introduces the concepts and results related to Markov chains with discrete time parameters. We also briefly discuss the reversible Markov chains at the end of the chapter.