In the last decade Markov-regime switching (MRS) models have been extensively used for modeling the unique behavior of spot prices in wholesale electricity markets. This popularity stems from the models’ relative parsimony and the ability to capture the stylized facts, in particular the mean reverting character of electricity spot prices, the regime changes implied by fundamentals and the resulting extreme price spikes. Due to the unobservable switching mechanism, the estimation of MRS models requires inferring model parameters and state process values at the same time. The situation becomes more complicated when the individual regimes are independent from each other and at least one of them is mean-reverting. Statistical validation of such models is also non-trivial. In this paper we review the available techniques and suggest efficient tools for statistical inference of MRS models.