The paper studies the generalized Procrustes problem with the extension of variable weights. A distinction is made between configurations and configuration classes, depending on the type of data used. The main interest is in configuration classes. Two different types of weighting are introduced: the Huber and Tukey weights. The objective is to use the weights to yield a resistant procedure for the generalized Procrustes problem. An algorithm is described and some attention is paid to the starting values in order to avoid non‐optimal solutions. An example and simulation results are presented which show that the weighted approach outperforms least squares when there are outliers in the dat