The new particle accelerators and its experiments create a challenging data processing environment, characterized by large amount of data where only small portion of it carry the expected new scientific information. Modern detectors, such as the Cathode Strip Chamber (CSC), achieve high accuracy of coordinate measurements (between 50 to 70 microns). However, heavy physical backgrounds can decrease the accuracy significantly. In the presence of such background, the charge induced over adjacent CSC strips (cluster) is different from the ideal Matheison distribution. The traditional least squares method which takes the same ideal position error for all clusters loses its optimal properties on contaminated data. A new technique that calculates the cluster quality and uses it to improve the track fitting results is suggested. The algorithm is applied on test beam data, and its performance is compared to other fitting methods. It is shown that the suggested algorithm improves the fitting performance significantly.