Good management in animal production systems is becoming of paramount importance. The aim of this paper was to develop a dynamic monitoring system for farrowing rate. A farrowing rate model was implemented using a dynamic generalized linear model (DGLM). Variance components were pre-estimated using an expectation-maximization (EM) algorithm applied on a dataset containing data from 15 herds, each of them including insemination and farrowing observations over a period ranging from 150 to 800 weeks. The model included a set of parameters describing the parity-specific farrowing rate and the re-insemination effect. It also provided reliable forecasting on weekly basis. Statistical control tools were used to give warnings in case of impaired farrowing rate. For each herd, farrowing rate profile, analysis of model components over time and detection of alarms were computed. The model provided a good overview of the development of the parity specific farrowing rate over time and the control charts were able to detect impaired results. Suggestions for future improvements include addition of parity-specific control charts, calibration of the charts for use in practice and inclusion of a sow effect in the farrowing model.