Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has been proven very successful for non-linear and non-Gaussian estimation problems. However, for the tracking of non-rigid objects, the selection of reliable image features is also essential. This paper presents the integration of color distributions into particle filtering, which has typically used edge-based image features. Color distributions are applied as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. Thus, the target model of the particle filter is defined by the color information of the tracked object. As the tracker should find the most probable sample distribution, the model is compared with the current hypotheses of the particle filter using the Bhattacharyya coefficient, which is a popular similarity measure between two distributions. The proposed tracking method directly incorporates the scale and motion changes of the objects. Comparisons with the well known mean shift tracker show the advantages and limitations of the new approach.