Prior research has extensively documented functional selectivity for categories within visual cortical areas, primarily by contrasting neural responses to images from various categories. However, such categorical approaches are less suitable to capture the diversity of neural representations within these areas. Do category-selective areas encode holistic categories, or are they instead tuned to multifaceted features? To address this question, we employed non-negative matrix factorization (NMF) for analyzing human fMRI responses to natural images in face-, body-, and scene-selective areas, which uncovered a consistent set of interpretable neural dimensions across participants. These dimensions not only aligned with the areas’ respective category preferences, but also revealed finer within-category distinctions, indicating selective tuning to diverse visual input features. Mapping these dimensions onto the cortical surface showed both clustered and distributed topographies, which accounted for overlaps between areas. Our results suggest that category-selective areas show multifaceted feature tuning, challenging traditional views and highlighting the complex interplay of neural dimensions in encoding visual information.