Electrocardiographic (ECG) signals are frequently corrupted by impulsive noise due to muscle activities, and background normalization is often needed to correct for patient motion and respiration. Nonlinear signal processing methods are effective alternatives to conventional linear filtering methods when dealing with impulsive noise or noise types that are difficult to characterize. The class of nonlinear filtering methods studied in this article operate by moving a window of finite width along the input data sequence. At each position, the filter output is obtained from the input samples inside the window. Nonlinear operators differ from linear filters in that the output is not a simple linear combination of the input samples. Three classes of nonlinear operators--median filters, morphologic operators, and the alpha-trimmed mean filter--are briefly introduced and algorithms using them for ECG signal processing are presented. Empirical results indicate that the nonlinear operators are good candidates for impulsive noise suppression and background normalization in ECG signal processing.