This article describes a connectionist language model, which may be used as an alternative to the well known n-gram models. A comparison experiment between n-gram and connectionist language models is performed on a Polish text corpus. Statistical language modeling is based on estimating a joint probability function of a sequence of words in a given language. This task is made problematic due to a phenomenon known commonly as the “curse of dimensionality”. This occurs because the sequence of words used to test the model is most likely going to be different from anything present in the training data. Classic solutions to this problem are successfully achieved by using n-grams which generalize the data by concatenating short overlapping word sequences gathered from the training data. Connections models, however, can accomplish this by learning a distributed representation for words. They can simultaneously learn both the distributed representation for each word in the dictionary as well as the synaptic weights used for modeling the joint probability of word sequences. Generalization can be obtained thanks to the fact that if a sequence is made up of words that were already seen, it will receive a higher probability than an unseen sequence of words. In the experiments, perplexity is used as measure of language model quality.