Multi-beamecho sounder systemhas been around nowfor some 40 years and their use in shallow waters for the last 14 years. With modern shallow water systems running at up to 9,600 soundings/second, data collection at the rate of approximately 250 million soundings/day system is possible. Processing of Multibeam Echo sounder (MBES) data is a challenging task from both hydrographic and technological perspectives. We recognize that a completely automatic system is improbable, but propose that significant benefits can still be had if we can automatically process good quality data, and highlight areas that probably need further attention. We propose an algorithm that takes uncleaned MBES data and attempts to pick out outliers as possible as we can. The traditionalmethod that is still in use today by numerous software applications is based on a line-by-line processing approach. Automatically filtering for a depth window, by beam number, slope between points, quality flags and recently by whether the beam¡¦s error is outside the IHO order for the survey are a number of ways in which the line-by-line approach has been speeded up. The fundamental differences between our method and the previous methods are that our algorithm does not actually delete any soundings at all and transform original one dimension information into two dimensions. Finally, we use Hierarchical Clustering to classifyMBES data into outliers and normal. We develop the user interface formulti-beamecho sounder quality control. It provides almost the necessary tools and modules to perform a survey. Standard modules are Survey planning (track guidance lines, waypoints), channel design and 3D modeling, data acquisition, data QC and data processing/flagged. However, it will visualize the soundings to aid the decisionmaking process.