This repository introduces the RadIOCD (Radar-based Interior Object Classification Dataset), which contains sparse point cloud representations of interior objects, collected by subjects wearing a commercial off-the-shelf mmWave radar. RadIOCD includes the recording of 10 volunteers, aged between 25 and 50 years old. A total amount of 5 objects, with the participants moving towards them in 2 different environments were recorded. RadIoCD includes sparse 3D point cloud data, together with their doppler velocity provided by the mmWave radar. The files were stored in CSV format to ensure its reuse. The scope of RadIoCD is the availability of data for the recognition of objects solely recorded by the mmWave radar, to be used in applications were the vision-based classification is not robust (e.g, in search and rescue operation where there is smoke inside a building). Furthermore, we showcase that this dataset containsenough data to apply Machine Learning-based techniques, and ensure that it could generalize in different environments and "unseen" subjects.