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In this paper, the operating states of a wheel loader were studied for diagnostics purposes using a real time simulation model of an articulated-frame-steered wheel loader. Test drives were carried out to obtain measurement data, which were then analyzed. The measured time series data were analyzed to find the sequences of operating states using two different data sets, namely the variables of hydrostatic transmission and working hydraulics. A time series is defined as a collection of observations made sequentially in time. In our proposed method, the time series data were first segmented to find operating states. One or more segments build up an operating state. A state is defined as a combination of the patterns of the selected variables. The segments were then clustered and classified. The operating states were further analyzed using the quantization error method to detect anomalies.