Semantic analysis of multimedia content is an ongoing research area that has gained a lot of attention over the last few years. Additionally, machine learning techniques are widely used for multimedia analysis with great success. This work presents a combined approach aiming at the semantic adaptation of neural network classifiers in a multimedia framework. Our proposal is based on a fuzzy reasoning engine which is able to evaluate the outputs and the confidence levels of the neural network classifier, using a domain specific knowledge base. The results obtained by the fuzzy reasoning engine are used as input for the adaptation of the network classifier, further increasing its ability to provide accurate classification of the specific content. The improved performance of the adapted neural network is used by a semantic segmentation algorithm that merges neighboring regions satisfying certain criteria. In that way, fine image segmentation and classification are established.