This paper presents the results obtained in a real experiment for object recognition in a sequence of images captured by a mobile robot in an indoor environment. Objects are simply represented as an unstructured set of spots (image regions) for each frame, which are obtained from the result of an image segmentation algorithm applied on the whole sequence. In a previous work, neural networks were used to classify the spots independently as belonging to one of the objects of interest or the background from different spot features (color, size and invariant moments). In this work, clustering techniques are applied afterwards taking into account both the neural net outputs (class probabilities) and geometrical data (spot mass centers). In this way, context information is exploited to improve the classification performance. The experimental results of this combined approach are quite promising and better than the ones obtained using only the neural nets.