Time series often contain breakpoints of di erent origin, i.e. breakpoints, caused by (i) shifts in trend, (ii) other changes in trend and/or, (iii) changes in variance. In the present study, arti cially generated time series with white and red noise structures are analyzed using three recently developed breakpoint detection methods. The time series are modi ed so that the exact "locations" of the arti cial breakpoints are prescribed, making it possible to evaluate the methods exactly. Hence, the study provides a deeper insight into the behaviour of the three di erent breakpoint detection methods. Utilizing this experience can help solving breakpoint detection problems in reallife data sets, as is demonstrated with two examples taken from the elds of paleoclimate research and petrology.