This study presents a summary of application of non-linear filters in signal processing, based on robust estimation theory. Non-linear filters are used in many applications including speech and image processing, mainly because of their ability to suppress noise and preserve signal features such as edges. This is a property that linear filters do not have. In the past several years, many types of non-linear filters have been propsed. However, each of these filters shows optimal smoothing efficiency only for a specific type of noise or a specific type of image. In order to further apply these filters in speech and image processing properly, it is important to conduct a review and analysis of all these filters and give an objective evaluation of their performance. Based upon the point estimation theory, three classes of estimations can be distinguished, namely L-estimators, which are based on linear combination of order statistics; R-estimators, which are derived from rank tests; and M-estimators - maximum likelyhood estimators. The first part of the work carried out in this study is classification of various approaches of non-linear filters in these three types of estimators according to the behavior of the filter itself. The second part is the computer implementation of all the filters discussed. Finally the summary of experimental results are presented.