Clustering techniques are often used in order to identify particular patterns in data and they can work as an efficient preliminary step for data exploration. In re- cent years, due to the fact that it becomes easier to store and to process large amount of data, researchers started to work on the so called functional data, proposing new functional clustering approaches. This same process, however, did not happen in the case of Biclustering, although the existence of a flourishing literature about these methods in the multivariate setting. In fact, even now, Biclustering methods are still strongly connected to the multivariate nature of gene expression data. In this work first attempts to deal with Biclustering for functional data are presented. New possi- ble techniques to obtain biclusters in a functional environment are explained from a theoretical and algorithmic points of view. To show the importance of the introduc- tion of these methods to the world of FDA, some real problem applications will be analyzed and shown in their results.