Tracking of poorly defined, rotating and/or distorted objects in a video sequence poses significant problems especially in medical diagnostics including ultrasound (sonographic) video used for examination and diagnosis of internal movement of tissue or muscle and nerve action. Cross-correlation techniques have been successful in retrieving dynamic information directly from ultrasound video data. We outline a fast implementation of tracking based on normalized cross-correlation using an adaptive template and present results from our application, developed in MATLAB™, which successfully tracks arbitrarily selected objects in deformed or severely compromised images. Common ultrasound image evaluation is qualitative but there is need to retrieve quantitative dynamic information such as the trajectory or velocity of selected areas. Our approach uses normalized two-dimensional cross-correlation to find the position of an initially selected template enclosing the feature of interest and map its trajectory frame-by-frame to produce displacement and velocity plots. We illustrate operation of the application using routine ultrasound data and demonstrate its performance using test video of objects rotating full circle and rolling down a ramp. We analyse errors associated with sampling to compare performance of our implementation with a more rigorous but tedious and computationally expensive correlation of a resampled, rotated, and shifted template.