Although older people are an important user group for smart environments, there has been relatively little work on adapting natural language interfaces to their requirements. In this paper, we focus on a particularly thorny problem: processing speech input from older users. Our experiments on the MATCH corpus show clearly that we need age-specific adaptation in order to recognize older users’ speech reliably. Language models need to cover typical interaction patterns of older people, and acoustic models need to accommodate older voices. Further research is needed into intelligent adaptation techniques that will allow existing large, robust systems to be adapted with relatively small amounts of in-domain, age appropriate data. In addition, older users need to be supported with adequate strategies for handling speech recognition errors.