This paper argues for using ambiguity plane features within dynamic statistical models for classification problems. The relative contribution of the two model components are investigated in the context of acoustically monitoring cutter wear during milling of titanium, an application where it is known that standard static classification techniques work poorly. Experiments show that explicit modeling of long-term context via a hidden Markov model state improves performance, but mainly by using this to augment sparsely labeled training data. An additional performance gain is achieved by using the shorter-term context of ambiguity plane features.