We propose a test-bed application for synthesis and analysis of multi-layeredmicroscopy data with variation in depth of focus(DOF), where we considerthe problem of detecting object overlap.For the synthesis part, the objects are elliptical in appearance with the possibilityof setting dierent parameters like noise, resolution, illumination,circularity, area and orientation.For the analysis part, the approach allows the user to set several parameters,including sensitivity for error calculation and classier type for analysis.We provide a novel algorithm that exploits the multi-layered nature of theobject overlap problem in order to improve recognition. The variation of grayvalue for each pixel in dierent depth is used as feature source for classication.The classier divides the pixels in three dierent groups: backgroundpixels, pixels in single cells and pixels in overlapping parts.We provide experimental results on the synthesized data, where we add noiseof dierent density. In non-noisy environments the performance for accuracyof overlapping positions is 93% and the performance of the missed overlapsis around 99.98% for density of 150 cells.iv