Fodor and Pylyshyn argued that connectionist models could not be used to exhibit and explain a phenomenon that they termed systematicity, and which they explained by possession of composition syntax and semantics for mental representations and structure sensitivity of mental processes. This inability of connectionist models, they argued, was particularly serious since it meant that these models could not be used as alternative models to classical symbolic models to explain cognition. In this paper, a connectionist model is used to identify some properties which collectively show that connectionist networks supply means for accomplishing a stronger version of systematicity than Fodor and Pylyshyn opted for. It is argued that 'context-dependent systematicity' is achievable within a connectionist framework. The arguments put forward rest on a particular formulation of content and context of connectionist representation, firmly and technically based on connectionist primitives in a learning environment. The perspective is motivated by the fundamental differences between the connectionist and classical architectures, in terms of prerequisites, lower-level functionality and inherent constraints. The claim is supported by a set of experiments using a connectionist architecture that demonstrates both an ability of enforcing, what Fodor and Pylyshyn term systematic and nonsystematic processing using a single mechanism, and how novel items can be handled without prior classification. The claim relies on extended learning feedback which enforces representational context dependence.