In this work, we present a novel 3D indirect shape analysis method which successfully retrieves 3D shapes based on hand-object interaction. To this end, the human hand information is first transferred to the virtual environment by the Leap Motion controller. Position-, angle- and intersection-based novel features of the hand and fingers are used for this part. In the guidance of these features that define the way humans grab objects, a support vector machine (SVM) classifier is trained. Experiments validate that SVM results are useful for retrieval of 3D shapes. We also compare the retrieval performance of our method with an interaction-based indirect method based on the Data Glove controller as well as a direct method based on 3D shape distribution histograms. These comparisons reveal different advantages of our method, which are (i) being lower-cost and more accurate compared to the Data Glove, and (ii) being more discriminative compared to a direct approach. We finally note that our algorithm is rigid-motion invariant and able to explore databases of arbitrarily represented 3D shapes.