This paper summarizes the development of equipment and techniques used for automated capture of floc images and measurement of floc properties. Methods for floc analysis are described which may be used equally well in the field or in the laboratory. The Matlab Image Processing Toolbox is used as a front end for applying several algorithms designed to clarify the images and measure the flocs. An iterative function provides threshold values to separate flocs from varying background light conditions in a stable and repeatable manner. The methods described can provide real-time analysis without user intervention. Matlab and IDL routines have been designed to measure settling velocity. The use of totally automated tracking over measurements made by a graphical user interface approach results in vastly more efficient measurements. The automated method also provides better data for very small and very slow-moving flocs. The graphical user interface approach, on the other hand, does not lead to some of the errors associated with the automated tracking. Both methods provide similar information about floc number, size, shape, and settling velocity, which can be used to calculate properties such as excess density, porosity, and fractal dimensions. Finally, kernel density estimation is introduced as a method to make meaningful interpretations from large amounts of highly variable data.