Hierarchical Self-Organizing Networks are used to reveal the topology and structure of datasets. Those structures create crisp partitions of the dataset producing branches or prototype vectors that represent groups of data with similar characteristics. However, when observations can be represented by several prototypes with similar accuracy, crisp partitions are forced to classify it in just one group, so crisp divisions usually lose information about the real dataset structure. To deal with this challenge we propose the Fuzzy Growing Hierarchical Self-Organizing Networks (FGHSON). FGHSON are adaptive networks which are able to reflect the underlying structure of the dataset, in a hierarchical fuzzy way. These networks grow by using three variables which govern the membership degree of data observations to its prototype vectors and the quality of the network representation. The resulting structure allows to represent heterogeneous groups and those that present similar membership degree to several clusters.