An approach to the analysis of data that contains (multiple) structural changes in a linear regression setup is presented. Various strategies which have been suggested in the literature for testing against structural changes as well as a dynamic programming algorithm for the dating of the breakpoints are implemented in the R statistical software package. Using historical data on Nile river discharges, road casualties in Great Britain and oil prices in Germany, it is shown that statistically detected changes in the mean of a time series as well as in the coefficients of a linear regression coincide with identifiable historical, political or economic events which might have caused these breaks.