We propose a new nonparametric technique for clutter rejection. We consider the Doppler data sampled using a sufficiently large dynamic range to allow for the clutter rejection to be implemented on the digital side. The Doppler signal is modeled as the summation of the true velocity signal, a clutter component, and a random noise component. To simplify the analysis, the first two components are assumed as deterministic yet unknown signals. The Doppler data are collected from the sample volume of interest as well as from several sample volumes in its neighborhood. Given that the shape of the clutter component will be similar in all these signals and given its relatively higher magnitude, it is possible to separate this component using principal component analysis (PCA). In particular, the clutter component appears as the first eigenvector (principal component) in PCA. Given this principal component, the projection of the Doppler signal of interest onto this component is removed and the remaining signal is subsequently used to derive the Doppler spectrogram. We describe an efficient implementation methodology that allows the added computational complexity of the new system to be reasonable.