A new feature set and decision function are proposed for classifying transient wandering-tone signals. Signals are partitioned in time and modeled as having piecewise-linear instantaneous frequency and piecewise-constant amplitude. The initial frequency, chirp rate, and amplitude are estimated in each segment. The resulting sequences of estimates are used as features for classification. The decision function employs a linear Gaussian dynamical model, or hidden Gauss-Markov model (HGMM). The parameters that characterize the HGMM for each class are estimated from labeled training sequences, and the trained models are used to evaluate the class-conditional likelihoods of an unlabeled signal. The signal is assigned to the class whose model gives the maximum conditional likelihood. Simulation experiments demonstrate perfect classification performance in a three-class forced-choice problem.