In this paper, we address the trajectory classification problem in Gaussian process framework without using Gaussian process based classification directly. Properties of the function corresponding to a trajectory are captured into the hyperparameters of a Gaussian process. As different trajectories have different properties, hyperparameters are different for these trajectories. In the hyperparametric space, different clusters are formed for noisy, shifted versions of the trajectories. The hyperparameters are used as features representing a trajectory and the classification task is performed in the hyperparametric space. Classification performance of the proposed method is evaluated on simulated data and also on realworld time series data.