In this paper, processing of sonar signals has been carried out using
the Minimal Resource Allocation Network (MRAN) and the Probabilistic Neural Network (PNN) in differentiating of commonly encountered features in indoor environments. The stability-plasticity behavior of both networks has been investigated. The experimental result shows that the MRAN possesses lower network complexity but experiences higher plasticity in comparison with PNN. The study also shows that the MRAN performance is superior in terms of on-line learning to PNN.