A new method for the automated detection of sperm whale clicks that combines neural network and statistical computations is presented. This method is intended to detect regular clicks and creaks and can be broken down into two main processing stages. The first stage works with the spectrogram output by computing the accumulated energy along each time frame, extracting consecutive two-seconds length time windows, obtaining statistical parameters characterizing these time windows and classifying them using a feed forward neural network as either containing regular clicks, creaks or noise. In the final stage a dynamic energy-based criterion is applied to each classified time windows based on previously computed statistical parameters. The performance of the method has been tested with three long recordings containing regular clicks and creaks and shows significantly high percentages of correct detections (global score of 94.8%) with a reduced computation time.