Particle filtering is being investigated extensively due to its important feature of target tracking based on nonlinear and non-Gaussian model. It tracks a trajectory with a known model at a given time. It means that particle filter tracks an arbitrary trajectory only if the time instant when trajectory switches from one model to another model is known apriori. Because of this reason particle filter is not able to track any arbitrary trajectory where transition from one model to another model is not known. For real world application, trajectory is always random in nature and may follow more than one model. In this paper we propose a novel method, which overcomes the above problem. In the proposed method a multiple model based approach is used along with particle filtering, which automates the model selection process for tracking an arbitrary trajectory. In the proposed approach, there is no need to have apriori information about the exact model that a target may follow. For data association, Markov Random Field (MRF) based method has been utilized. It allows us to exploit the neighborhood concept for data association, i.e. the association of a measurement influences an association of its neighbor measurement.