To fulfill its functions, RNA must fold into specific three-dimensional structures. Consequently, we need to predict the 3D-structure of an RNA. Some interactions, called canonical interactions, and forming the local secondary structure, are effectively predicted nowadays. However, other interactions are essential to the good global folding of the molecule, and stay currently unpredictable. The most well-known class is the A-minor like motifs, whereas they have been proven to play a crucial role in folding and cellular mechanisms from ribozymes to ribosomes by riboswitches and viral RNAs. We developed an automated pipeline of exhaustive extraction and classification of A-minor like motifs in RNA 3D-structures. The classification is done by comparing the local context of these motifs, i.e. the arrangement of bonds around the motifs. Each motif and its context are represented by a special graph, where nucleotides are grouped according to their proximity and to the bounds they are involved in. A special similarity measure has been developed for comparing them. We first show that there is a fairly good correlation between our similarity measure and the RMSD. Then, our first results show that, as expected, homologous occurrences share similar contexts. More interestingly, a few number of non-homologous occurrences also does.