The properties of the Haar Transform (HT) are discussed based on the Wavelet Transform theory. A system with two channels at resolution 2-1 and 2-2 for detecting paroxysm-spike in human's EEG is presented according to the multiresolution properties of the HT. The system adopts a coarse-to-fine strategy. First, it performs the coarse recognition on the 2-2 channel for selecting the candidate of spike in terms of rather relaxed criterion. Then, if the candidate appears, the fine recognition on the 2-1 channel is carried out for detecting spike in terms of stricter criterion. Three features of spike are extracted by investigating its intrinsic properties based on the HT. In the case of having no knowledge of prior probability of the presence of spike, the Neyman-Pearson criteria is applied to determining thresholds on the basis of the probability distribution of background and spike obtained by the results of statistical analysis to minimize error probability. The HT coefficients at resolution 2-2 and 2-1 can be computed individually and the data are compressed with 4:1 and 2:1 respectively. A half wave is regarded as the basic recognition unit so as to be capable of detecting negative and positive spikes simultaneously. The system provides a means of pattern recognition for non-stationary signal including sharp variation points in the transform domain. It is specially suitable and efficient to recognize the transient wave with small probability of occurrence in non-stationary signal. The practical examples show the performance of the system.