This paper presents a comparison of several methods for constructing neural classifiers in the framework of mobile robot positioning. The task of positioning a mobile robot using a ring of sonars or laser sensors is used as a testbed for different radial basis function neural network constructive algorithms that produce topological maps of the environment. The experiments performed using two different experimental systems show the advantages and drawbacks of the compared algorithms.