Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. Each neurule is represented as an adaline unit. One way that the neurules can be produced is from training examples (empirical source knowledge). However, in certain application fields not all of the training examples are available a priori. A number of them become available over time. In these cases, updating the corresponding neurules is necessary. In this paper, methods for updating a hybrid rule base, consisting of neurules, to reflect the availability of new training examples are presented. The methods are efficient, since they require the least possible retraining effort and the number of the produced neurules is kept as small as possible.