For centuries, rosemary (Rosmarinus officinalis L.) has been used to prepare essential oils which, even now, are highly valued due to their various biological activities. Nevertheless, it has been noted that these activities often depend on the origin of the rosemary plant and the method of extraction. Since both of these quality parameters can greatly influence the chemical composition of rosemary oil, an original analytical method was developed where "dry distillation" was coupled to headspace solid-phase microextraction (HS-SPME) and then a data mining technique using the Kohonen self-organizing map algorithm was applied to the data obtained. This original approach uses the newly described microwave-accelerated distillation technique (MAD) and HS-SPME; neither of these techniques require external solvent and so this approach provides a novel "green" chemistry sampling method in the field of biological matrix analysis. The large data set obtained was then treated with a rarely used chemometric technique based on nonclassical statistics. Applied to 32 rosemary samples collected at the same time from 12 different sites in the north of Algeria, this method highlighted a strong correlation between the volatile chemical compositions of the samples and their origins, and it therefore allowed the samples to be grouped according to geographical distribution. Moreover, the method allowed us to identify the constituents that exerted the most influence during classification.