Agroenergy, a relatively simple and mature technology to convert biomass into heat and electric energy, may represent a good opportunity to introduce the biorefinery schemes in rural areas. However, to guarantee the feasibility of new investments in this innovative sector, the commitment of all relevant players, and the sharing of their embedded knowledge of local conditions will play a crucial role. In this paper, we propose a modified neural network model to analyse the knowledge extracted from different groups of actors, in order to prevent the definition of strategic plans which may not be not fully consistent. We propose a methodology to support the strategic planning of the agroenergy innovation deployment in rural areas, based on the logical framework of the SWOT analysis, through which the most relevant factors affecting the expectations of local informed actors are identified. Subsequently, a modified multilayered feed-forward neural network is proposed to analyse the qualitative data, in order to verify their consistency. The results obtained from a case study in the province of Foggia (Italy) show that the level of consistency between the perceived factors affecting the deployment of the technology and the expectations towards the successful adoption of agroenergy at local level may vary depending on the degree of involvement and commitment of local players. This may represent a relevant issue for the definition of long-term strategic planning.