The presence of outliers corrupts the procedure of dynamic data reconciliation. In this note, a cluster analysis technique is suggested as a way for discriminating outliers and normal observation data. Furthermore, the formulation of the dynamic data reconciliation problem is modified to incorporate the outlier information. In this way, dynamic reconciliation can be carried out simultaneously with outlier detection. The performance of the proposed approach is demonstrated by simulations on a chemical engineering example from literature.