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In rock drilling, as in many industries today, the drive towards unmanned equipment and full automation is a big issue. A challenge in the automation process for rock drilling is the retraction of the drill steels when the drilling is completed. Today the drilling can be performed automatically to some extend, but a human ear is required for the final part: when the splices between the drill steels are opened up enough to allow retraction. This paper discusses a Fast Fourier Transform (FFT) method to search through audio data in order to detect and locate specific sounds appearing when retraction of the drill steels is possible, and to investigate if achieving full automation of the drilling process is possible. The use of Wavelets has also been evaluated. As far as the authors know, there is no system today for automatic retraction of the drill steels. By recording and analysing sounds from rock drill rigs, a comparison between a system implemented with an electronic ear and a human ear has been evaluated. The FFT has been applied as a pre-processing method and examines features of power spectrum for the detection of the sound, when the splices are opened up. This sound contains higher power spectrum than sounds from the rest of the drilling procedure. Using these features, a classification program has been designed. The experimental results shows that there is a good possibility to make a commercialized product that automatically detect when the drill steels are ready to be retracted.