In vehicle design it is desirable to model the loads by describing the load environment, the customer usage and the vehicle dynamics. In this study a method will be proposed for detection of curves using a lateral acceleration signal. The method is based on hidden Markov models (HMMs) which are probabilistic models that can be used to recognize patterns in time series data. In an HMM, 'hidden' refers to a Markov chain where the states are not observable, however what can be observed is a sequence of data where each observation is a random variable whose distribution depends on the current hidden state. The idea here is to consider the current driving event as the hidden state and the lateral acceleration signal as the observed sequence. Examples of curve detection are presented for both simulated and measured data. The classification results indicate that the method can recognize left and right turns with small misclassification errors.