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To get ultrasound color flow images of high quality, it is important to suciently suppress the clutter signals originating from stationary and slowly moving tissue. Without sucient clutter rejection, low velocity blood flow can not be measured, and estimates of higher velocities will have a large bias. The small number of samples available (8-16) makes clutter ltering in color flow imaging a challenging problem. In this paper we review and analyze three classes of lters: FIR, IIR, and regression lters. The quality of the lters was assessed based on the frequency response, as well as on the bias and variance of a mean blood velocity estimator using an autocorrelation technique. For FIR lters, the frequency response was improved by allowing a non-linear phase response. By estimating the mean blood flow velocity from two vectors ltered in the forward and backward direction, respectively, the standard deviation was signi cantly lower with a minimum phase lter than with a linear phase lter. For IIR lters applied to short signals, the transient part of the output signal is important. We analyzed zero, step, and projection initialization, and found that projection initialization gave the best lters. For regression lters, polynomial basis functions provide eective clutter suppression. The best lters from each of the three classes gave comparable bias and variance of the mean blood velocity estimates. However, polynomial regression lters and projection initialized IIR lters had a slightly better frequency response than could be obtained with FIR lters.