Computerized detection of epileptiform transients (ETs), also called spikes and sharp waves, in the electroencephalogram (EEG) has been a research goal for the last 40years. A reliable method for detecting ETs could improve efficiency in reviewing long EEG recordings and assist physicians in interpreting routine EEGs. Computer algorithms developed so far for detecting ETs are not as reliable as human expert interpreters, mostly due to the large number of false positive detections. Typical methods for ET detection include measuring waveform morphology, detecting signal non-stationarity, and power spectrum analysis. Some progress has been made by using more advanced algorithmic approaches including wavelet analysis, artificial neural networks, and dipole analysis. Comparing the performance of different algorithms is difficult since each study uses its own EEG test dataset. In order to overcome this problem, European researchers in the field of computerized electrocardiogram interpretation organized a large multi-center research workgroup to create a standardized dataset of ECG recordings which were interpreted by a large group of cardiologists. EEG researchers need to follow this as a model and seek funding for the creation of a standardized EEG research dataset to develop ET detection algorithms and certify commercial software.