Pointing is one of the communicative actions that infants acquire during their first year of life. Based on a hypothesis that early pointing is triggered by emergent reaching behavior toward objects placed at out‐of‐reach distances, we proposed a neural network model that acquires reaching without explicit representation of ‘targets’. The proposed model controls a two‐joint arm in a horizontal plane, and it learns a loop of internal forward and inverse transformations; the former predicts the visual feedback of hand position and the latter generates motor commands from the visual input through random generation of the motor commands. In the proposed model, the motor output and visual input were represented by broadly tuned neural units. Even though explicit ‘targets’ were not presented during learning, the simulation successfully generated reaching toward visually presented objects at within‐reach and out‐of‐reach distances. We proposed a neural network model that acquires reaching without explicit representation of ‘targets’. The proposed model learns a loop of internal forward andinverse transformations. Even though explicit ‘targets’ were not presented during learning, the simulation successfully generated reaching toward visually presented objects at within‐reach and out‐of‐reach distances.