Hearing loss is one of the most significant sensory disabilities. It can have various negative effects on a person's quality of life, ranging from impeded school and academic performance to total social isolation in severe cases. It is therefore vital that early symptoms of hearing loss are diagnosed quickly and accurately. Audiology tests are commonly performed with the use of tonal audiometry, which measures a patient's hearing threshold both in air and bone conduction at different frequencies. The graphic result of this test is represented on an audiogram, which is a diagram depicting the values of the patient's measured hearing thresholds. In the course of the presented work several different artificial neural network models, including MLP, CNN and RNN, have been developed and tested for classification of audiograms into two classes -normal and pathological represented hearing loss. The networks have been trained on a set of 2400 audiograms analysed and classified by professional audiologists. The best classification performance was achieved by the RNN architecture (represented by simple RNN, GRU and LSTM), with the highest out-of-training accuracy being 98% for LSTM. In clinical application, the developed classifier can significantly reduce the workload of audiology specialists by enabling the transfer of tasks related to analysis of hearing test results towards general practitioners. The proposed solution should also noticeably reduce the patient's average wait time between taking the hearing test and receiving a diagnosis. Further work will concentrate on automating the process of audiogram interpretation for the purpose of diagnosing different types of hearing loss.