This paper presents a multi-map recurrent neural architecture, exhibiting self-organization to deal with the partial observations of the phase of some dynamical system. The architecture captures the dynamics of the system by building up a representation of its phases, coping with ambiguity when distinct phases provide identical observations. The architecture updates the resulted representation to adapt to changes in its dynamics due to self-organization property. Experiments illustrate the dynamics of the architecture when fulfilling this goal.