Functional connectivity MRI (fcMRI) is a technique used to study the functional connectedness of distinct regions of the brain by measuring the temporal correlation between their blood oxygen level dependent (BOLD) contrasts. fcMRI is typically measured with Pearson correlation (PC), but the measure of linear correlation between BOLD contrasts can be negatively affected from natural phenomena such as differences in the rate of activation between different brain regions. As an alternative metric for measuring the similarity between time series that is robust to such time lags, we assess the potential of dynamic time warping (DTW) for measuring functional connectivity. We used PC fcMRI data and DTW fcMRI data as predictors in machine learning models for classifying participants with Autism Spectrum Disorders (ASD) from those who are typically developing (TD) and found results suggesting that DTW fcMRI data is as informative as PC fcMRI for ASD classification. When combined with dimensional reduction techniques, such as principal components analysis, DTW fcMRI showed greater predictive ability than PC fcMRI data. Our results suggest that DTW fcMRI can be a suitable alternative metric that may be characterizing fcMRI in a different, but complementary, way to PC fcMRI that is worth continued investigation.