Using micro-data provided by statistical agencies has many benefits from the data mining point of view. However, such data often involve sensitive information that can be directly or indirectly related to individuals. An appropriate anonymisation process is needed to minimize the risk of disclosure. Several masking methods have been developed to deal with continuous-scale numerical data or bounded textual values but approaches to tackling the anonymisation of textual values are scarce and shallow. Because of the importance of textual data in the information society, in this paper the authors present a new masking method for anonymising unbounded textual values based on the fusion of records with similar values to form groups of indistinguishable individuals.