The use of non-testing strategies like read-across in the hazard assessment of chemicals and nanomaterials (NMs) is deemed essential to perform the safety assessment of all NMs in due time and at lower costs. The identification of physicochemical (PC) properties affecting the hazard potential of NMs is crucial, as it could enable to predict impacts from similar NMs and outcomes of similar assays, reducing the need for experimental (and in particular animal) testing. This manuscript presents a review of approaches and available case studies on the grouping of NMs to read-across hazard endpoints. We include in this review grouping frameworks aimed at identifying hazard classes depending on PC properties, hazard classification modules in control banding (CB) approaches, and computational methods that can be used for grouping for read-across. The existing frameworks and case studies are systematically reported. Relevant nanospecific PC properties taken into account in the reviewed frameworks to support grouping are shape and surface properties (surface chemistry or reactivity) and hazard classes are identified on the basis of biopersistence, morphology, reactivity, and solubility.