The current noise control methods are not sufficient for covering the ratings of different kinds of noise. They are mainly based on experiments with stationary and artificial test signals. New judgment models have to be developed to account for the time fluctuations of environmental sounds. During the last decade algorithms emerged in connectionism that not only find optimal mappings within stationary data (for example the backpropagation algorithm) but also within time series. One architecture for this task is called finite impulse response (FIR) neural network. Due to the architecture the networks include preceding time steps for the calculation of the current output. This contribution presents FIR neural networks to model sound quality judgments on environmental sounds. A listening test has been conducted for collecting training samples for the network and for testing its generalization abilities. The sound patterns and the subjects answers are pre-processed using factor analysis. The analysis of the subjects answers yielded 2 factors, which can be identified to represent the sound attributes annoying and powerful. The FIR neural networks are trained to estimate both attributes. The results of training and generalization show high correlation between the sound attributes and predicted judgments of the neural networks.