Several progresses have been made in evaluating the development policies for rural areas in the last years; many indicators1 have been set for assessing the effectiveness of Common Agricultural Policy (CAP) and Rural Development Policies (RDPs) and their role on the convergence process of the EU members, but a shared definition of rurality is still missing. The results obtained at the level of growth and development by the most lagging behind areas, are far from being satisfactory (Brasili, 2005). The evaluation of the policies and programmes introduced evidenced lack of institutional planning and implementing abilities, and an insufficient targeting of policies and payments (Mantino, 2010). The experience of the 10 New Member States (NMSs)2 showed how the current CAP and Cohesion policy, designed for the EU-15 (Csaki et al. 2010), aren’t enough for addressing the regional specificities, hindering a process of development which is already weakened by the effects of the unfinished transition. This paper aims at offering a methodological contribution for evaluating the EU membership, with particular attention to the CAP, in Hungary. We chose this Country among the 10 NMSs because of the relevance (96%) of the rural areas on the total land3, and given the historical socio-economic role played by agriculture. The authors believe that more targeted – and therefore efficient – policies for agricultural and rural areas require a deeper knowledge of their structural and dynamic characteristics. Therefore, in order to identify the changes occurred before (2003) and after (2007) the EU membership on agricultural and rural areas, we use the following multivariate statistics methodologies: Principal Components Analysis, applied to the set of 42 variables, and Cluster Analysis on the results obtained by the Principal Components Analysis. Then, we offer a preliminary evaluation of the distribution of Single Area Payment Scheme (SAPS)4, using the information on the applications provided at the County level by the Hungarian Paying Agency to show correlations with the leading factors.