A study of health of a human being is an ever interesting and fertile one for any re-searcher. This is one such study in which we use Markov chain for the modeling of the problem that identifies the health status of an individual through his mood on two successive days. We have performed an empirical study for which we have collected data regarding the mood change in two successive days by making a questionnaire and identify the Markov chain structure from the data. Then we have analyzed the Markov chain to calculate higher order transition probabili-ties and steady state probabilities. Using those, we made some analytical inferences and for that we have used R language and MS Excel. Based on the data, we have found that the Markov chain, so formed is ergodic in nature with high steady state probability for happy state which is one of the six moods we have assumed when collecting the data. We have also classified the dif-ferent states or moods of the data. From this study, we also infer that this is a simple way of tran-sitioning across the states or moods of a human. Also, using this line of analysis is a robust, easy and efficient for one in calculating measures regarding moods or mood disorders.