Adaptive multichannel prediction-error filtering is compared to conventional optimum Wiener filtering for 10 types of array data. Adaptive maximum-likelihood signal extraction is compared to Wiener filtering for three sets of data the three sets are composed of actual signal, artificial signal with varying magnitude and velocity, and a composite of noise data. Comparison of the two methods is based on total mean-square-error and the distribution of the error power with frequency. Online adaptive processing will solve problems with slowly time-varying noise fields such as UBO road noise. The adaptive method is also simpler and more economical than the Wiener method as an off-line filter design procedure for array data known to be approximately time stationary. The two methods will produce essentially equivalent filters with respect to total mean-square-error however, relatively large differences in the actual filter response characteristics are possible.