Different artificial networks are presented with the task of learning weak noim declension in G e r m a n . This morphological rule is difficult for cue-based models because it requires the resolution of conflicting cue-predictions and a dynamic positional coding due to suffixation. In addition to that its 'task frequency* is very low in natural language. This property is preserved in the training input to study the models' abilities to handle low frequency niles. The performances of three kinds of networks: 1) feedforward networks 2) recurrent networks 3) recurrent networks with short term memory ( S T M ) capacity are compared to empirical findings of an elicitation experiment with 129 subjects of ages 5-9 and adult age.