A goal of data matching is to define homologous geographic features in two differents sources, features representing the same object from the real world. The **MultiCriteriaMatching** data matching algorithm requires defining a reference and a comparison dataset giving in this way the direction of matching (for each feature from the reference dataset, the algorithm looks for homologous features in the comparison dataset). Let us mention that the reference dataset can be either an authoritative or a crowdsourced dataset. Knowing the characteristics of our datasets, criteria can be choosen (*e.g.* position, toponym, semantic, etc.). Each criteria must be associated with a similarity measure. For example, the position criterion is based on the distance between the reference feature and a candidate (*e.g.* Euclidean distance for landmarks and average of minimum of Hausdorff distance between every roads segments for lines), the name criterion compares the name of the reference feature with the name of the candidate (different measures exists to compare strings, Samal distance (Samal et al., 2005) and Cosinus distance is considered as most appropriate for points and itinerary), semantic criterion compares feature types, etc. All these criteria are merged to take a final decision in the process, the **MultiCriteriaMatching** algorithm do not take any decision (i.e. features are not matched) if the criteria are contradictories; these cases are tagged as uncertainty.