In this paper we introduce a new method of deciding, if a trajectory is following a pre-defined path. This is achieved by representing hypotheses as trajectories themselves using Accumulated State Densities. Live tracking data is incorporated into the trajectories via out of sequence processing. Through this, we gain two representations of the sensor data, each conditioned on a hypotheses. By using an adapted version of the sequential likelihood ratio test, we can test which hypotheses is more likely and therefore arrive at a decision. Our method can be used for e.g. surveillance of sea lane traffic or other kinds of object movements where a strict adherence to a path is necessary. Our approach can easily be incorporated into existing sensor data fusion methods as most of the calculation is derived from usual tracking algorithms. Simulations show, that the approach is capable of delivering reliable decisions even with high noise and a low frequency of measurements.