Various nonlinear, fixed-neighborhood techniques for filtering color images based on local statistics have been proposed in the literature. We present adaptive neighborhood filtering (ANF) techniques for noise removal in color images. The main idea is to find for each pixel in the image (called the 'seed' when being processed) a variable-shaped, variable-sized neighborhood that contains only pixels that are similar to the seed. Then, statistics computed using pixels within the adaptive neighborhood are used to derive the filter output. Results of the ANF techniques are compared with those given by a few multivariate fixed-neighborhood filters: the double- window modified trimmed-mean filter, the generalized vector directional filter -- double-window -- (alpha) -trimmed mean filter, the adaptive hybrid multivariate filter, and the adaptive non-parametric filter with Gaussian kernel. It is shown that the ANF techniques provide the best visual results, effectively suppressing noise while not blurring edges. The ANF results are also the best in terms of objective measures (such as normalized mean-squared error and normalized color difference).