An acoustic echo cancellation structure with a single loudspeaker and multiple microphones is, from a system identification perspective, generally modelled as a single-input multiple-output system. Such a system thus implies specific echo-path models (adaptive filter) for every loudspeaker to microphone path. Due to the often large dimensionality of the filters, which is required to model rooms with standard reverberation time, the adaptation process can be computationally demanding. This paper presents a selective updating normalized least mean square (NLMS)-based method which reduces complexity to nearly half in practical situations, while showing superior convergence speed performance as compared to conventional complexity reduction schemes. Moreover, the method concentrates the filter adaptation to the filter which is most misadjusted, which is a typically desired feature.