Three adaptive multichannel L-filters based on marginal data ordering are proposed. They rely on well-known algorithms for the iterative minimization of the mean square error (MSE), namely, the least mean squares (LMS), the normalized LMS, and the LMS-Newton algorithms. We treat both the unconstrained minimization of the MSE and the minimization of the MSE when structural constraints are imposed on the filter coefficients. The performance of the proposed adaptive multichannel L-filters is compared to that of other multivariate nonlinear filters in color image filtering. Adaptive multichannel linear filters and adaptive single- channel L-filters are considered as well. Performance comparisons are made in both RGB and U*V*W* color spaces. The proposed adaptive multichannel L-filters outperform the other candidates in noise suppression for color images corrupted by mixed impulsive and additive white contaminated Gaussian noise.