In this paper, we studied the problem of feature-based motion tracking in echocardiographic image sequences. We described the relation between possible feature variations and different kinds of tissue motion using a linear convolution model. We also showed that motion-feature decorrelation (which means that the motion parameters estimated using feature tracking fail to represent the underlying tissue motion) compensation is an ill-posed inverse problem. Instead of finding a method that may provide better compensation results than previous approaches, we used an quantitative measure to compare the reliability of tracking features. Experiment results showed that the use of the reliability measure improved the robustness of displacement estimation. With the help of the reliability measure, we compared the performance of different features using simulations and phantom examples. While we noticed that the radio frequency (RF) signal outperforms the B-mode (BM) signal in the analysis of small deformation (e.g., less than 0.1% compression), we also found out that the BM signal works better than the RF signal in the analysis of large deformation (e.g., larger than 2% compression). The use of a band-passed filtered feature does not result in significant improvement in tracking.