This paper introduces a neuro-fuzzy system for the estimation of the crowding level in a scene. Monitoring the crowding level, i.e. the number of people present in a given indoor environment, is a requirement in a variety of surveillance applications. In the present work, crowding has to be estimated from the image processing of visual scenes collected via a TV camera. A suitable pre-processing of the images, along with an ad-hoc feature extraction process is discussed. Estimation of the crowding level in the feature space is described in terms of a fuzzy decision rule, which relies on the membership of input patterns to a set of partially-overlapping crowding classes, comprehensive of ‘doubt’ classifications and ‘outliers’. A society of neural networks, either multilayer perceptrons or hyper-radial basis functions, is trained to model individual class-membership functions. Integration of the neural nets within the fuzzy decision rule results in an overall neuro-fuzzy classifier. Important topics concerning the generalization ability, the robustness, the adaptivity and the performance evaluation of the system are explored. Experiments with real-world data were accomplished, comparing the present approach with statistical pattern recognition techniques, namely linear discriminant analysis and nearest neighbor. Experimental results validates the neuro-fuzzy approach to a large extent. The system is currently successfully working as a part of a monitoring system in Dinegro underground station in Genoa, Italy