Do you mean the "input output and curve fitting" neural network type from nnstart? Since in that network, no delayed information about previous inputs/outputs is used, you can simply add all your datasets in one big matrix:
Example input u and output y (where 1 and 2 are two seperately measured datasets):
u1 = [1 2 3 4 5] y1 = [1 1 1 2 1]
u2 = [2 2 3 2 3] y2 = [3 3 3 4 3]
X = [u1 u2] T = [y1 y2]
and train the nn with input X and output T. Be aware that you still need to convert these matrices to a nn dataset with tonndata(). Watch the dimensions of your data, e.g. if it is a vertical vector instead of the shown horizontal vector. If you are not familiar with the training commands, type in nnstart and let the tool help you.
However, if you want to use a nn as mathematical model for a dynamic system (engineering background, machinery, some electrical filter), then you must use the dynamic ones from the nnstart toolbox "dynamic time series". Since this kind of nn reacts to past events, multiple datasets for training cannot be simply put together - it would look like your physical system would "jump" every time a new dataset it coming. Instead, for dynamic nn you must use catsample to make the training algorithm acknowledge that there are multiple, separated datasets. You can see the effect of catsample in the following screenshot: