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The uniaxial compressive strength (UCS) of intact rock is one of the most important parameters required and determined for rock mechanics studies in engineering projects. The limitations and difficulty of conducting tests on rocks, specifically on thinly bedded, highly fractured, highly porous and weak rocks, as well as the fact that these tests are destructive, expensive and time-consuming, lead to development of soft computing-based techniques. Application of artificial neural networks (ANNs) for predicting UCS has become an attractive alternative for geotechnical engineering scientists. In this study, an ANN was designed with the aim of indirectly predicting UCS through the serpentinization percentage, and physical, dynamic and mechanical characteristics of serpentinites. For this purpose, data obtained in earlier experimental work from central Greece were used. The ANN-based results were compared with the experimental ones and those obtained from previous analysis. The proposed ANN-based formula was found to be very efficient in predicting UCS values and the samples could be classified with simple physical, dynamic and mechanical tests, thus the expensive, difficult, time-consuming and destructive mechanical tests could be avoided.