OBJECTIVES The objective of this study is to develop and validate a conditional logistic regression model for the prediction of locoregional recurrence (LRR) of breast cancer. To make a translation to  clinical practice a web based nomogram was made. METHODS Women first diagnosed with early breast cancer (without distant metastasis or ingrowth in the chest wall or skin) between 2003-2006 were selected from the Netherlands Cancer Registry (n=39,929). Risk factors for LRRs within five year of the primary treatment were determined using logistic regression. Risks were determined per year, conditional on not being diagnosed with recurrence in the previous year. The presence of interaction and collinearity in the nomogram was assessed, as well as the discrimination by means of the area under the ROC curve and calibration by the Hosmer-Lemeshow goodness-of-fit test in deciles. Data on primary tumours diagnosed between 2007-2008 from a selection of Dutch hospitals was used for external validation of the performance of the nomogram (n=13,792). RESULTS The final model included the variables grade, size, multifocality, and nodal involvement of the primary tumour, type of surgery, and whether patients were treated with radio-, chemo- or hormone therapy. The modelling group showed an area under the ROC curve of 0.82, 0.74, 0.67, 0.70 and 0.60 respectively per subsequent year after primary treatment. The calibration was sufficient. All effects in the validation group were in the same direction, and the estimates in the validation group did not differ significantly from the modelling group. CONCLUSIONS This validated nomogram can be used as an instrument to aid clinical decision-making and to identify patients with a high risk of breast cancer recurrence who might benefit from a more intensive follow-up after breast cancer.