The olive trees (Olea europaea L.) have been cultivated for millennia in the Mediterranean basin and its oil has been an important part of human nutrition in the region. In order to distinguish between olive accessions, morphological and biological characters have been widely and commonly used for descriptive purposes and have been used to characterize olive accessions. A comparative study of morphological characters of olive accessions grown in Tunisia was carried out and analyzed using Bayesian Networks (BN) and Principal Components Analysis (PCA). The obtained results showed that averages of fruit and kernel weights were 2.27 grams and 0.41 grams, respectively. Besides, a relatively moderate level of variation (51.22%) being explained by four Principal components. BN revealed that geographical localisation plays a role in the increase of tree habit, size of lenticels and leaf shape. A dendrogram has been carried out in the aim to classify studied olive accessions. We proposed a novel method of analysis based on the three-step scheme, in which first the data set is clustered, then olive tree features are evaluated. The studied accessions can be divided into four main groups by cutting the dendrogram at a similarity value of 0.645. Different relationships are studied and highlighted, and finally the collected features are subjected to a global principal component analysis. Obtained results confirmed that core surface was negatively correlated with geographical location (r = -0.52, p<0.05) and maturation period r = -0.539, p<0.05). Number of lenticels was positively correlated to lenticels size (r = 0.632, p<0.05). Core shape had a negative correlation with fruit shape (r = -0.759, p<0.05). On the basis of these findings, this research confirmed that morphological markers are a preliminary tool to characterize olive oil accessions.