The challenge for agricultural policymakers and planners, particularly in the context of Rwanda with high population density and consequently food insecurity, is how to enable farmers to adopt new technology. It is known that adoption of new technology may vary among farm households because of socio-economic characteristics. This paper intends to typify farm households in Rwanda based on the exploration of factors explaining adoption of new technology. Ultimately, typical farms obtained from the typology will be used, later as basis to develop representative mathematical programming models. Multivariate statistical techniques offer the means of creating such typologies, particularly when an in-depth database is available. This multivariate analysis approach, combining principal component analysis (PCA) and cluster analysis (CA), has allowed us to identify clearly five typical farm households and their socio-economic characteristics explaining adoption of new technology.. Multivariate statistical techniques, such as PCA and CA, are great tools to envisage building mathematical programming models on the basis of typical farm households.