Data association and model selection are important factors for tracking multiple targets in a dense clutter environment. We propose a sequential probabilistic multiple hypotheses tracking (PMHT) based algorithm using interacting multiple model (IMM), namely IMM-PMHT algorithm. Inclusion of IMM enables to track any arbitrary trajectory without any apriori information about the target dynamics. IMM allows us to incorporate different dynamic models for the targets and PMHT helps to avoid the uncertainty about the measurement origin. It operates in an iterative mode using expectation-maximization (EM) algorithm. The proposed algorithm uses only measurement association as missing data, which simplifies E-step and M-step. It is computationally more efficient, and an important characteristic of our proposed algorithm is that it operates in a single batch model, i.e. sequential, and hence can be used for real time tracking.