In recent years, the number of human-machine interactions has increased considerably. Additionally, we have evidence of linguistic differences between human-machine interactions and human–human conversations (e.g., Timpe-Laughlin et al., 2022). Therefore, it is reasonable to revisit theoretical frameworks that conceptualize interactional language use and investigate to what extent they still apply to technology-mediated interactions. As a first attempt at exploring whether pragmatics theories apply to human-machine interaction, we examined how well Kecskés's (2013) socio-cognitive approach (SCA) focusing on asymmetric interactions (e.g., between interlocutors of different language backgrounds) applies to the asymmetry of human-machine interactions. Using examples from experimental data, we present the nature of common ground between human and machine (spoken dialogue system) interlocutors, focusing on the construction of and reliance on the emergent side of common ground that is informed by the actual situational experience. Like Kecskés, we argue that both egocentrism and cooperation play a role in human-machine interaction. While the former is manifested in approaching the machine interlocutor as if it was human, the latter appears to play a role in common ground seeking and building as well as in recipient design. We demonstrate that Kecskés's SCA is a fitting framework for analyzing human-machine communication contexts. •Kecskés's socio-cognitive approach (SCA) applies to human-machine interaction.•Both egocentrism and cooperation play a role in human-machine interaction.•Human interlocutors try to find core common ground, and seek to create emergent common ground in interaction with machines.•Emergent common ground is informed by situational experience.•Carefully planned recipient design and salience effect are present in data.