We present an algorithm for the automatic acquisition of salient grammar fragments in the form of finite state machines (FSMs). Salient phrase fragments are selected using a significance test, then clustered using a combination of string and semantic distortion measures. Each cluster is then compactly represented as an FSM. Flexibility is enhanced by permitting approximate matches to paths through each FSM. Multiple fragment detections are exploited by means of a neural network. The methodology is applied to the "How may I help you?" (HMIHY) call-type classification task.