Nonlinear characteristics of the Canadian surface air temperature (SAT) were investigated by applying a neural-network-based nonlinear principal component analysis (NLPCA) method to the SAT anomaly data for individual seasons. The SAT data were separated into three subsets: data for 1900--1949 and 1900--1995 over southern Canada (south of 60¿N), called S0049 and S0095, respectively, and data for 1950--1995 over the entire country, called C5095. The NLPCA was computed for the three data sets separately. The leading NLPCA modes from C5095 and S0095 show similar results: the nonlinearity is strong in winter (December, January, and February, DJF) and fall (September, October, and November, SON), but is much weaker in spring (March, April and May, MAM) and summer (June, July, and August, JJA), manifesting the seasonal dependence of the nonlinearity in the Canadian SAT. No significant nonlinearity is detected from data set S0049, even for the winter and fall seasons, indicating interdecadal dependence of the nonlinearity. The leading NLPCA mode combines the effects of Pacific-North America (PNA) pattern and North Atlantic Oscillation (NAO) on the Canadian winter SAT. A possible reason for the existence of nonlinearity in the winter SAT only after 1950 is that the NAO manifested its strong negative phase from the 1950s to the early 1970s.