Activated sludge process form an important part of wastewater treatment plant with domestic effluent. The variations in the state of the process are appeared as those in the size and structure of flocs and filaments found in the wastewater samples from aeration tank of secondary treatment. The normal operation requires proper settling of flocs in the secondary clarifier, which is affected by problem of bulking and pin point flocs. Conventional physico-chemical methods take a lot of time to detect the abnormal operation, consequently leaving insufficient time for precautionary measures. Image processing and analysis of microscopic images can offer a time-efficient alternative to monitor the operation of activated sludge process. Segmentation is a necessary part of image processing and analysis for identification of regions of interest in the image, and its acceptable accuracy is pre-requisite of the morphological analysis. In this paper, three segmentation techniques, fuzzy cmeans, k-means and Otsu thresholding, were used to segment flocs in microscopic images of samples taken from aeration tank of activated sludge process. The performance of the segmentation algorithms was evaluated for images taken at four different objective magnifications of microscope, using metrics of global consistency error (GCE), random index (RI) and variation of information (VI). The performance metrics were evaluated by comparing the segmented images with the approximation of ground truth images. Finally, the effect of magnification was investigated on the image segmentation and analysis procedure and observed that the size of floc, perceptible to the image segmentation and analysis procedure is greater and more precise at higher magnification. © 2014 IEEE.