Human activities have altered flow regimes resulting in increased pressures and threats on river biota. Physical habitat simulation has been established as a standard approach among the methods for Envi-ronmental Flow Assessment (EFA). Traditionally, in EFA, univariate habitat suitability curves have beenused to evaluate the habitat suitability at the microhabitat scale whereas Generalized Additive Mod-els (GAMs) and fuzzy logic are considered the most common multivariate approaches to do so. Theassessment of the habitat suitability for three size classes of the West Balkan trout (Salmo farioides; Karaman, 1938) inferred with these multivariate approaches was compared at three different levels. First the modelled patterns of habitat selection were compared by developing partial dependence plots. Then,the habitat assessment was spatially explicitly compared by calculating the fuzzy kappa statistic andfinally, the habitat quantity and quality was compared broadly and at relevant flows under a hypotheti-cal flow regulation, based on the Weighted Usable Area (WUA) vs. flow curves. The GAMs were slightlymore accurate and the WUA-flow curves demonstrated that they were more optimistic in the habitatassessment with larger areas assessed with low to intermediate suitability (0.2–0.6). Nevertheless, bothapproaches coincided in the habitat assessment (the optimal areas were spatially coincident) and inthe modelled patterns of habitat selection; large trout selected microhabitats with low flow velocity,large depth, coarse substrate and abundant cover. Medium sized trout selected microhabitats with lowflow velocity, middle-to-large depth, any kind of substrate but bedrock and some elements of cover. Finally small trout selected microhabitats with low flow velocity, small depth, and light cover onlyavoiding bedrock substrate. Furthermore, both approaches also rendered similar WUA-flow curves andcoincided in the predicted increases and decreases of the WUA under the hypothetical flow regulation.Although on an equal footing, GAMs performed slightly better, they do not automatically account forvariables interactions. Conversely, fuzzy models do so and can be easily modified by experts to includenew insights or to cover a wider range of environmental conditions. Therefore, as a consequence of theagreement between both approaches, we would advocate for combinations of GAMs and fuzzy models infish-based EFA.