Fuzzy logic programs are a useful framework for handling uncertainty in logic programming; nevertheless, there is the need for modelling adaptation of fuzzy logic programs. In this paper, we first overview weighted fuzzy programs, which bring fuzzy logic programs and connectionist models closer together by associating significance weights with the atoms of a logic rule: by exploiting the existence of weights, it is possible to construct a neural network model that reflects the structure of a weighted fuzzy program. Based on this model, we then introduce the weighted fuzzy program adaptation problem and propose an algorithm for adapting the weights of the rules of the program to fit a given dataset.