diff --git a/matlab/loadNN.m b/matlab/loadNN.m
new file mode 100644
index 0000000..bc7f966
--- /dev/null
+++ b/matlab/loadNN.m
@@ -0,0 +1,64 @@
+function net = loadNN(filename)
+    %Load neural network object from file
+    % 		
+    % 	Args:
+    % 		filename:	path to csv file to save neural network
+    %
+    %   Returns:
+    %       net:        neural network object
+
+    [~,~,rawData] = xlsread(filename);
+
+    %read neural network structure nn
+    nn = str2num(rawData{2,1});
+
+    %read input delays dIn
+    if isnumeric(rawData{4,1})
+       dIn = rawData{4,1};
+    else
+       dIn = str2num(rawData{4,1});
+    end
+    
+    %read iternal delays dIntern
+    if isnumeric(rawData{6,1})
+        dIntern = rawData{6,1};
+    elseif rawData{6,1}==','
+        dIntern = []; 
+    else
+        dIntern = str2num(rawData{6,1});
+    end
+   
+    %read output delays dOut
+    if isnumeric(rawData{8,1})
+        dOut = rawData{8,1};
+    elseif rawData{8,1}==','
+        dOut = []; 
+    else
+        dOut = str2num(rawData{8,1});
+    end
+    
+    %read factor for input data normalization normP
+    if isnumeric(rawData{10,1})
+       normP = rawData{10,1};
+    else
+       normP = str2num(rawData{10,1})';
+    end
+    
+    %read factor for output data normalization normY
+    if isnumeric(rawData{12,1})
+       normY = rawData{12,1};
+    else
+       normY = str2num(rawData{12,1})';
+    end
+    
+    %read weight vector w
+    w = csvread(filename,13,0);
+    
+    %Create neural network and assign loaded weights and factors
+    net = CreateNN(nn,dIn,dIntern,dOut);
+    net.normP = normP;
+    net.normY = normY;
+    net.w = w;
+    
+    
+end
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