Markov switching models are one possible method to account for volatility clustering. This chapter aims at describing, in a pedagogical fashion, how to estimate a univariate switching model for daily foreign exchange returns which are assumed to be drawn in a Markovian way from alternative Gaussian distributions with different means and variances. An application shows that the US dollar/Deutsche Mark exchange rate can be modelled as a mixture of normal distributions with changes in volatility, but not in mean, where regimes with high and low volatility alternate. The usefulness of this methodology is demonstrated in a real life application, i.e. through the performance comparison of simple hedging strategies.